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The risk of water erosion in mainland China is intensifying due to climate change. A high-precision rainfall erosivity dataset is crucial for revealing the spatiotemporal patterns of rainfall erosivity and identifying key areas of water erosion. However, due to the insufficient spatiotemporal resolution of historical precipitation data, there are certain biases in the estimation of rainfall erosivity in China, especially in regions with complex terrain and climatic conditions. Over the past decade, the China Meteorological Administration has continuously improved its ground-based meteorological observation capabilities, forming a dense network of ground-based observation stations. These high-precision precipitation data provide a solid foundation for quantifying the patterns of rainfall erosivity in China. In this study, we first performed rigorous quality control on the 1-minute ground observation precipitation data from nearly 70,000 stations nationwide from 2014 to 2022, ultimately selecting 60,129 available stations. Using the precipitation data from these stations, we calculated event rainfall erosivity and generated a national mean annual rainfall erosivity dataset with a spatial resolution of 0.25°. This dataset shows that the mean annual rainfall erosivity in mainland China is approximately 1241 MJ·mm·ha−1·h−1·yr−1, with areas exceeding 4000 MJ·mm·ha−1·h−1·yr−1 mainly concentrated in the southern China and southern Tibetan Plateau. Compared to our study, previously released datasets overestimate China's mean annual rainfall erosivity by 31 %~65 %, and there are significant differences in performance across different river basins. In summary, the release of this dataset facilitates a more accurate assessment of the current water erosion intensity in China. The dataset is available from the National Tibetan Plateau/Third Pole Environment Data Center (https://doi.org/10.11888/Terre.tpdc.301206 ; Chen, 2024). [ABSTRACT FROM AUTHOR]

Meteorological fields calculated by numerical weather prediction (NWP) models drive offline chemical transport models (CTMs) to solve the transport, chemical reactions, and atmospheric interaction over the geographical domain of interest. HARMONIE (HIRLAM ALADIN Research on Mesoscale Operational NWP in Euromed) is a state-of-the-art non-hydrostatic NWP community model used at several European weather agencies to forecast weather at the local and/or regional scale. In this work, the HARMONIE WINS50 (cycle 43 cy43) reanalysis dataset at a resolution of 0.025° × 0.025° covering an area surrounding the North Sea for the years 2019–2021 was coupled offline to the LOTOS-EUROS (LOng-Term Ozone Simulation-EURopean Operational Smog model, v2.2.002) CTM. The impact of using either meteorological fields from HARMONIE or from ECMWF on LOTOS-EUROS simulations of NO2 has been evaluated against ground-level observations and TROPOMI tropospheric NO2 vertical columns. Furthermore, the difference between crucial meteorological input parameters such as the boundary layer height and the vertical diffusion coefficient between the hydrostatic ECMWF and non-hydrostatic HARMONIE data has been studied, and the vertical profiles of temperature, humidity, and wind are evaluated against meteorological observations at Cabauw in The Netherlands. The results of these first evaluations of the LOTOS-EUROS model performance in both configurations are used to investigate current uncertainties in air quality forecasting in relation to driving meteorological parameters and to assess the potential for improvements in forecasting pollution episodes at high resolutions based on the HARMONIE NWP model. [ABSTRACT FROM AUTHOR]

Accurate long-term daily cloud-gap-filled fractional snow cover products are essential for climate change and snow hydrological studies in the Asian Water Tower (AWT) region, but existing Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover products are not sufficient. In this study, the multiple-endmember spectral mixture analysis algorithm based on automatic endmember extraction (MESMA-AGE) and the multistep spatiotemporal interpolation algorithm (MSTI) are used to produce the MODIS daily cloud-gap-filled fractional snow cover product over the AWT region (AWT MODIS FSC). The AWT MODIS FSC products have a spatial resolution of 0.005° and span from 2000 to 2022. The 2745 scenes of Landsat-8 images are used for the areal-scale accuracy assessment. The fractional snow cover accuracy metrics, including the coefficient of determination (R2), root mean squared error (RMSE) and mean absolute error (MAE), are 0.80, 0.16 and 0.10, respectively. The binarized identification accuracy metrics, including overall accuracy (OA), producer's accuracy (PA) and user's accuracy (UA), are 95.17 %, 97.34 % and 97.59 %, respectively. Snow depth data observed at 175 meteorological stations are used to evaluate accuracy at the point scale, yielding the following accuracy metrics: an OA of 93.26 %, a PA of 84.41 %, a UA of 82.14 % and a Cohen kappa (CK) value of 0.79. Snow depth observations from meteorological stations are also used to assess the fractional snow cover resulting from different weather conditions, with an OA of 95.36 % (88.96 %), a PA of 87.75 % (82.26 %), a UA of 86.86 % (78.86 %) and a CK of 0.84 (0.72) under the MODIS clear-sky observations (spatiotemporal reconstruction based on the MSTI algorithm). The AWT MODIS FSC product can provide quantitative spatial distribution information on snowpacks for mountain hydrological models, land surface models and numerical weather prediction in the Asian Water Tower region. This dataset is freely available from the National Tibetan Plateau Data Center at 10.11888/Cryos.tpdc.272503 (Jiang et al., 2022) or from the Zenodo platform at 10.5281/zenodo.10005826 (Jiang et al., 2023a). [ABSTRACT FROM AUTHOR]

Glaciers in the Alps are losing mass at unprecedented and accelerating rates. Monitoring of glacier mass change as well as relevant atmospheric parameters plays an important role in improving understanding of local and downstream impacts. We present subseasonal, seasonal, and annual glaciological mass balance data and meteorological observations from Mullwitzkees and Venedigerkees, two glacier monitoring sites in the Hohe Tauern range of the Austrian Alps. Ablation stake networks were established on Mullwitzkees in 2006/07 and on Venedigerkees in 2011/12. Monitoring is ongoing. In addition to stake readings at subseasonal intervals, accumulation measurements (snow pits and probing) are carried out seasonally. The glaciological data set consists of subseasonal floating date measurements as well as fixed date seasonal and annual values. Fixed date glacier wide mass balance was derived from annual point mass balance values. Automatic weather stations measuring standard meteorological parameters were installed near Mullwitzkees and Venedigerkees in 2020 and 2019, respectively. Meteorological data is provided in 10 minute intervals. Uncertainties for individual point mass balance measurements were computed following the approach of the Swiss glacier monitoring service (GLAMOS), taking into account estimated density and reading errors. The subseasonal mass balance records highlight shorter term variability in mass loss and the linkage with meteorological conditions. The most negative annual point mass balance recorded in the period of record was -5.8 ± 0.66 m w.e. at an elevation of 2536 on Venedigerkees. 2022 stands out as the most negative mass balance year to date in both time series, particularly at higher elevations. The cumulative specific mass balance (glacier wide) over the period of record was -14.68 m w.e. at Mullwitzkees and -8.79 m w.e. at Venedigerkees. Data is available in PANGAEA publication series and the associated datasets. The main publication series are updated annually. The Mullwitzkees mass balance datasets can be found at: doi: 10.1594/PANGAEA.965660 and doi: 10.1594/PANGAEA.965719. The Venedigerkees data can be found at doi: 10.1594/PANGAEA.965648 and doi: 10.1594/PANGAEA.965729. [ABSTRACT FROM AUTHOR]

Regional-scale surface energy balance (SEB) models of glacier melt require forcing by coarse-gridded data from reanalysis or global climate models that need to be downscaled to glacier scale. As on-glacier meteorological observations are rare, it generally remains unknown how exact the reanalysis and downscaled data are for local-scale SEB modeling. We address this question by evaluating the performance of reanalysis from the European Centre for Medium-Range Weather Forecasts (ERA5 and ERA5-Land reanalysis), with and without downscaling, at four glaciers in western Canada with available on-glacier meteorological measurements collected over different summer seasons. We dynamically downscale ERA5 with the Weather Research and Forecasting (WRF) model at 3.3 and 1.1 km grid spacing. We find that our SEB model, forced separately with the observations and the two reanalyses, yields less than 10 % difference in simulated total melt energy and shows strong correlations (0.86) in simulated time series of daily melt energy at each site. The good performance of the reanalysis-derived melt energy is partly due to cancellation of biases between overestimated incoming shortwave radiation and substantially underestimated wind speed and subsequently turbulent heat fluxes. Downscaling with WRF improves the simulation of wind speed, while other meteorological variables show similar performance to ERA5 without downscaling. The choice of WRF physics parameterization schemes is shown to have a relatively large impact on the simulations of SEB components but a smaller impact on the modeled total melt energy. The results increase our confidence in dynamical downscaling with WRF for long-term glacier melt modeling in this region. [ABSTRACT FROM AUTHOR]

Accurate long-term daily Cloud-gap-filled fractional snow cover products are essential for climate change and snow hydrological studies in the Asia Water Tower (AWT) region, but existing Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover products are not sufficient. In this study, the multiple endmember spectral mixture analysis algorithm based on automatic endmember extraction (MESMA-AGE) and the multistep spatiotemporal interpolation algorithm (MSTI) are used to produce the MODIS daily cloud-gap-filled fractional snow cover product over the AWT region (AWT MODIS FSC). The AWT MODIS FSC product have a spatial resolution of 0.005°, and spans from 2000 to 2022. The 2745 scenes of Landsat-8 images are used for the areal scale accuracy assessment. The fractional snow cover accuracy metrics, including coefficient of determination (R2 ), root mean squared error (RMSE), and mean absolute error (MAE) are 0.80, 0.16 and 0.10, respectively. The binarized identification accuracy metrics, including overall accuracy (OA), producer’s accuracy (PA), and user’s accuracy (UA), are 95.17%, 97.34% and 97.59%, respectively. Snow depth data observed at 175 meteorological stations are used to evaluate accuracy at point scale, yielding the following accuracy metrics: an OA of 93.26%, a PA of 84.41%, a UA of 82.14%, and a cohen’s kappa (CK) value of 0.79. Snow depth observations from meteorological stations are also used to assess the fractional snow cover resulting from different weather conditions, with an OA of 95.36% (88.96%), a PA of 87.75% (82.26%), a UA of 86.86% (78.86%) and a CK of 0.84 (0.72) under the MODIS clear sky observations (spatiotemporal reconstruction based on the MSTI algorithm). The AWT MODIS FSC product can provide quantitative spatial distribution information of snowpack for mountain hydrological models, land surface models, and numerical weather prediction in the Asia Water Tower region. [ABSTRACT FROM AUTHOR]

Aerosol lidar networks can play an important role in revealing structural characteristics of the atmospheric boundary layer, the urban heat island effect, and the spatial distribution of aerosols, especially in relation to the monitoring of atmospheric pollution in megacities. To fulfill the need of the monitoring and numerical forecasting of atmospheric pollution, an aerosol lidar network is proposed by the China Meteorological Administration which serves as an important part of the "MegaCity Experiment on Integrated Meteorological Observation in China" (MEMO). To ensure a high standard of data quality and traceability of measurement error, an inter-comparison campaign, dedicated to the quality assessment of lidar systems from different institutes and manufacturers, was designed and performed at Beijing Southern Suburb Observatory in September 2021. Six Mie–Rayleigh lidar systems at 1064 nm were involved in this campaign. The strategies for lidar self-evaluations and inter-comparisons were predefined. A lidar system at 1064 nm, which was developed by the Atmospheric Remote Sensing group at Wuhan University, was selected as the reference lidar system after passing all strict self-evaluation quality checks. The reference lidar system serves as the cornerstone for evaluating the performance of other lidar systems. After using the Rayleigh fit and signal-to-noise evaluation self-tests for each individual lidar system as a fast check of the data quality, the range-corrected signal and backscatter coefficient obtained from all the lidar systems were inter-compared with a reference lidar system. In the end, the lidar systems passed the quality control/assurance, ensuring that the standard deviation of range-corrected signal could be controlled within 5 % at 500–2000 m and 10 % at 2000–5000 m. For the derived aerosol backscatter coefficients, standard deviations can be controlled within 10 % at 500–2000 and 2000–5000 m. The quality assurance strategy lays down a solid basis for atmospheric lidar at near-infrared wavelengths and will be applied in Chinese lidar network development. [ABSTRACT FROM AUTHOR]

Generating spatial crop yield information is of great significance for academic research and guiding agricultural policy. Existing public yield datasets have a coarse spatial resolution, spanning from 1 to 43 km. Although these datasets are useful for analyzing large-scale temporal and spatial change in yield, they cannot deal with small-scale spatial heterogeneity, which happens to be the most significant characteristic of the Chinese farmers' economy. Hence, we generated a 30 m Chinese winter wheat yield dataset (ChinaWheatYield30m) for major winter-wheat-producing provinces in China for the period 2016–2021 with a semi-mechanistic model (hierarchical linear model, HLM). The yield prediction model was built by considering the wheat growth status and climatic factors. It can estimate wheat yield with excellent accuracy and low cost using a combination of satellite observations and regional meteorological information (i.e., Landsat 8, Sentinel 2 and ERA5 data from the Google Earth Engine (GEE) platform). The results were validated using in situ measurements and census statistics and indicated a stable performance of the HLM based on calibration datasets across China, with a correlation coefficient (r) of 0.81 and a relative root mean square error (rRMSE) of 12.59 %. With regards to validation, the ChinaWheatYield30m dataset was highly consistent with in situ measurement data and statistical data (p<0.01), indicated by an r (rRMSE) of 0.72** (15.34 %) and 0.69** (19.16 %). The ChinaWheatYield30m is a sophisticated dataset with both high spatial resolution and excellent accuracy; such a dataset will provide basic knowledge of detailed wheat yield distribution, which can be applied for many purposes including crop production modeling and regional climate evaluation. The ChinaWheatYield30m dataset generated from this study can be downloaded from 10.5281/zenodo.7360753 (Zhao et al., 2022b). [ABSTRACT FROM AUTHOR]

The fire weather index (FWI) is a widely used metric for fire danger based on meteorological observations. However, due to its empirical formulation based on a specific regional relationship between the meteorological observations and fire intensity, the ability of the FWI to accurately represent global satellite-derived fire intensity observations is limited. In this study, we propose a fire parameterization method using feed-forward neural networks (FFNNs) for individual grids. These FFNNs for each grid point utilize four daily meteorological variables (2-meter relative humidity (RH2m), precipitation, 2-meter temperature, and wind speed) as inputs. The outputs of the FFNNs are satellite-derived fire radiative power (FRP) values. Applying the proposed FFNNs for fire parameterization during the 2001-2020 period revealed a marked enhancement in cross-validated skill compared to parameterization solely based on the FWI. This improvement was particularly notable across East Asia, Russia, the eastern US, southern South America, and central Africa. The sensitivity experiments demonstrated that the RH2m is the most critical variable in estimating the FRP and its regional differences via the FFNNs. Conversely, the FWI-based estimations were primarily influenced by precipitation. The FFNNs accurately captured the observed nonlinear correlations between FRP and RH2m, as well as precipitation. In contrast, FWI-based estimations exhibit an excessively negative relationship between FRP and precipitation. [ABSTRACT FROM AUTHOR]

The main in situ database for numerical weather prediction currently relies on radiosonde and airliner observations, with large systematic data gaps: horizontally in certain countries, above the oceans and in polar regions, and vertically in the rapidly changing atmospheric boundary layer, as well as up to the tropopause in areas with low air traffic. These gaps might be patched by measurements with drones. They provide a significant improvement towards environment-friendly additional data, avoiding waste and without the need for helium. So far, such systems have not been regarded as a feasible alternative for performing measurements up to the upper troposphere. In this article, the development of a drone system that is capable of sounding the atmosphere up to an altitude of 10 km with its own propulsion is presented, for which Antarctic and mid-European ambient conditions were taken into account: after an assessment of the environmental conditions at two exemplary radiosounding sites, the design of the system and the instrumentation are presented. Further, the process to get permissions for such flight tests even in the densely populated continent of Europe is discussed, and methods to compare drone and radiosonde data for quality assessment are presented. The main result is the technical achievement of demonstrating the feasibility of reaching an altitude of 10 km with a small meteorologically equipped drone using its own propulsion. The first data are compared to radiosonde measurements, demonstrating an accuracy comparable to other aircraft-based observations, despite the simplistic sensor package deployed. A detailed error discussion is given. The article closes with an outlook on the potential use of drones for filling data gaps in the troposphere. [ABSTRACT FROM AUTHOR]

In this paper, the implementation and validation of the torus mapping, a method to dealias radial wind observations by meteorological radars is presented. We apply the procedure to data from the Operational program for exchange of weather radar information (OPERA) over central part of Europe for the whole year 2021. The quality of the resulting radar winds is assessed by a comparison to colocated radiosonde and aircraft observations and also through analysis of first guess departures using a regional numerical weather prediction model. Performance of quality control on dealiased radial wind data is also evaluated.We show that the torus mapping method is a robust procedure on data with a sufficiently low amount of measurement noise. It produces datasets of comparable quality for a wide range of Nyquist velocity values and has potential for operational applications. [ABSTRACT FROM AUTHOR]

This paper investigates how to refine the ground meteorological observation network to greatly improve the PM 2.5 concentration forecasts by identifying sensitive areas for targeted observations that are associated with a total of 48 forecasts in eight heavy haze events during the years of 2016–2018 over the Beijing–Tianjin–Hebei (BTH) region. The conditional nonlinear optimal perturbation (CNOP) method is adopted to determine the sensitive area of the surface meteorological fields for each forecast, and a total of 48 CNOP-type errors are obtained including wind, temperature, and water vapor mixing ratio components. It is found that, although all the sensitive areas tend to locate within and/or around the BTH region, their specific distributions are dependent on the events and the start times of the forecasts. Based on these sensitive areas, the current ground meteorological stations within and around the BTH region are refined to form a cost-effective observation network, which makes the relevant PM 2.5 forecasts starting from different initial times for varying events assimilate fewer observations, but overall, it achieve the forecasting skill comparable to and even higher than that obtained by assimilating all ground station observations. This network sheds light on the idea that some of the current ground stations within and around the BTH region are very useless for improving the PM 2.5 forecasts in the BTH region and can be greatly scattered to avoid unnecessary work. [ABSTRACT FROM AUTHOR]

Cosmic-ray neutron sensing (CRNS) allows for the estimation of root-zone soil water content (SWC) at the scale of several hectares. In this paper, we present the data recorded by a dense CRNS network operated from 2019 to 2022 at an agricultural research site in Marquardt, Germany – the first multi-year CRNS cluster. Consisting, at its core, of eight permanently installed CRNS sensors, the cluster was supplemented by a wealth of complementary measurements: data from seven additional temporary CRNS sensors, partly co-located with the permanent ones; 27 SWC profiles (mostly permanent); two groundwater observation wells; meteorological records; and Global Navigation Satellite System reflectometry (GNSS-R). Complementary to these continuous measurements, numerous campaign-based activities provided data by mobile CRNS roving, hyperspectral imagery via UASs, intensive manual sampling of soil properties (SWC, bulk density, organic matter, texture, soil hydraulic properties), and observations of biomass and snow (cover, depth, and density). The unique temporal coverage of 3 years entails a broad spectrum of hydro-meteorological conditions, including exceptional drought periods and extreme rainfall but also episodes of snow coverage, as well as a dedicated irrigation experiment. Apart from serving to advance CRNS-related retrieval methods, this data set is expected to be useful for various disciplines, for example, soil and groundwater hydrology, agriculture, or remote sensing. Hence, we show exemplary features of the data set in order to highlight the potential for such subsequent studies. The data are available at doi.org/10.23728/b2share.551095325d74431881185fba1eb09c95. [ABSTRACT FROM AUTHOR]

The Alpha Jet Atmospheric eXperiment (AJAX) flew scientific flights between 2011 and 2018 providing measurements of trace gas species and meteorological parameters over California and Nevada, USA. This paper describes the observations made by the AJAX program over 229 flights and approximately 450 h of flying. AJAX was a multi-year, multi-objective, multi-instrument program with a variety of sampling strategies resulting in an extensive dataset of interest to a wide variety of users. Some of the more common flight objectives include satellite calibration/validation (GOSAT, OCO-2, TROPOMI) at Railroad Valley and other locations and long-term observations of free-tropospheric and boundary layer ozone allowing for studies of stratosphere-to-troposphere transport and long-range transport to the western United States. AJAX also performed topical studies such as sampling wildfire emissions, urban outflow and atmospheric rivers. Airborne measurements of carbon dioxide, methane, ozone, formaldehyde, water vapor, temperature, pressure and 3-D winds made by the AJAX program have been published at NASA's Airborne Science Data Center (https://asdc.larc.nasa.gov/project/AJAXTS9 (last access: 1 November 2022), 10.5067/ASDC/SUBORBITAL/AJAX/DATA001, Iraci et al., 2021a). [ABSTRACT FROM AUTHOR]

In situ meteorological data are essential to better understand ongoing environmental changes in the Arctic. Here, we present a dataset of quality-controlled meteorological observations by two automatic weather stations in northwest Greenland from July 2012 to the end of August 2020. The stations were installed in an accumulation area on the Greenland Ice Sheet (SIGMA-A site, 1490 m a.s.l.) and near the equilibrium line of the Qaanaaq Ice Cap (SIGMA-B site, 944 m a.s.l.). We describe the two-step sequence of quality-control procedures that we used to create increasingly reliable datasets by masking erroneous data records. We analyzed the resulting 2012-2020 time series of air temperature, positive degree-days, snow height, surface albedo, and histograms of longwave radiation (a proxy of cloud formation frequency). We found that snow height increased and albedo remained steady at the SIGMA-A site, whereas high air temperatures and clear-sky conditions prevailed while snow height and albedo decreased in the summers of 2015, 2019, and 2020 at the SIGMA-B site. Therefore, it appears that these weather conditions led to notable snow height degradation at the SIGMA-B site but not at the SIGMA-A site. We anticipate that this quality-control method and these datasets will aid in climate studies of northwest Greenland as well as contribute to the advancement of broader polar climate studies. [ABSTRACT FROM AUTHOR]

Spread across Twente and its neighbouring regions in the east of the Netherlands, a network of 20 profile soil moisture and temperature (5, 10, 20, 40, and 80 cm depths) monitoring stations was established in 2009. Field campaigns have been conducted covering the growing seasons of 2009, 2015, 2016, and 2017, during which soil sampling rings and handheld probes were used to measure the top 5 cm volumetric soil moisture content (VSM) of 28 fields near 12 monitoring stations. In this paper, we describe the design of the monitoring network and the field campaigns, adopted instrumentation, experimental setup, field sampling strategies, and the development of sensor calibration functions. Maintenance and quality control procedures and issues specific to the Twente network are discussed. Moreover, we provide an overview of open third-party datasets (i.e. land cover/use, soil information, elevation, groundwater, and meteorological observations) that can support the use and analysis of the Twente soil moisture and temperature datasets beyond the scope of this contribution. An indication for the spatial representativeness of the permanent monitoring stations is provided through comparisons of the 5 cm station measurements with the top 5 cm field-averaged VSM derived from the field campaign measurements. The results reveal in general reasonable agreements and root mean squared errors that are dominated by underestimations of the field-averaged VSM, which is particularly apparent for the grass fields and is strong after heavy rain. Further, we discuss the prospects the datasets offer to investigate (i) the reliability of soil moisture references that serve the development and validation of soil moisture products, and (ii) the water and energy exchanges across the groundwater–vadose-zone–atmosphere continuum within a lowland environment in a changing climate. The datasets discussed are publicly available at 10.17026/dans-znj-wyg5 (Van der Velde et al., 2022). [ABSTRACT FROM AUTHOR]

The amount and phase of cold season precipitation accumulating in the upper Saint John River basin are critical factors in determining spring runoff, ice-jams, and flooding in downstream communities. To study the impact of winter and spring storms on the snowpack in the upper Saint John River (SJR) basin, the Saint John River Experiment on Cold Season Storms (SAJESS) utilized meteorological instrumentation, upper air soundings, human observations, and hydrometeor macrophotography during winter/spring 2020-21. Here, we provide an overview of the SAJESS study area, field campaign, and existing data networks surrounding the upper SJR basin. Initially, meteorological instrumentation was co-located with an Environment and Climate Change Canada station near Edmundston, New Brunswick, in early December 2020. This was followed by an intensive observation period that involved manual observations, upper-air soundings, a multi-angle snowflake camera, macrophotography of solid hydrometeors, and advanced automated instrumentation throughout March and April 2021. The resulting 25 datasets include optical disdrometer size and velocity distributions of hydrometeors, micro rain radar output, near-surface meteorological observations, and wind speed, temperature, pressure and precipitation amounts from a K63 Hotplate precipitation gauge, the first one operating in Canada. These data are publicly available from the Federated Research Data Repository at https://doi.org/10.20383/103.0591 (Thompson et al., 2022). We also include a synopsis of the data management plan and data processing, and a brief assessment of the rewards and challenges of utilizing community volunteers for hydro-meteorological citizen science. [ABSTRACT FROM AUTHOR]

The paper investigates how to refine the ground meteorological observation network for greatly improving the PM2.5 concentration forecasts by identifying the sensitive areas for targeted observations associated with a total of 48 forecasts in eight heavy haze events during the years of 2016- 2018 over the Beijing-Tianjin-Hebei (BTH) region. The conditional non-linear optimal perturbation (CNOP) method is adopted to determine the sensitive area of the surface meteorological fields for each forecast and a total of 48 CNOP-type errors are obtained including wind, temperature, and water vapor mixing ratio components. It is found that, although all the sensitive areas tend to locate within and/or surrounding the BTH region, their specific distributions are dependent on the events and the start times of the forecasts. Based on these sensitive areas, the current ground meteorological stations within and surrounding the BTH region are refined to form a cost-effective observation network, which makes the relevant PM2.5 forecasts starting from different initial times for varying events assimilate fewer observations but overall achieve the forecasting skill comparable to, even higher than that obtained by assimilating all ground station observations. This network sheds light on that some of the current ground stations within and surrounding the BTH region are very useless for improving the PM2.5 forecasts in the BTH region and can be greatly scattered to avoid the thankless work. [ABSTRACT FROM AUTHOR]

The Alpha Jet Atmospheric eXperiment (AJAX) flew scientific flights between 2011 and 2018 providing measurements of trace gas species and meteorological parameters over California and Nevada, USA. This paper describes the observations made by the AJAX program over 229 flights and approximately 450 hours of flying. AJAX was a multi-year, multi-objective, multi-instrument program with a variety of sampling strategies resulting in an extensive dataset of interest to a wide variety of users. Some of the more common flight objectives include satellite calibration/validation (GOSAT, OCO-2, TROPOMI) at Railroad Valley and other locations, and long-term observations of free-troposphere and boundary layer ozone allowing for studies of stratosphere-to-troposphere transport and long-range transport to the western United States. AJAX also performed topical studies such as sampling wildfire emissions, urban outflow, and atmospheric rivers. Airborne measurements of carbon dioxide, methane, ozone, formaldehyde, water vapor, temperature, pressure, and 3-D winds made by the AJAX program have been published at NASA's Airborne Science Data Center. [ABSTRACT FROM AUTHOR]

A novel fiber-optic distributed temperature sensing instrument, the Fiber-optic Laser Operated Atmospheric Temperature Sensor (FLOATS), was developed for continuous in situ profiling of the atmosphere up to 2 km below constant-altitude scientific balloons. The temperature-sensing system uses a suspended fiber-optic cable and temperature-dependent scattering of pulsed laser light in the Raman regime to retrieve continuous 3 m vertical-resolution profiles at a minimum sampling period of 20 s. FLOATS was designed for operation aboard drifting super-pressure balloons in the tropical tropopause layer at altitudes around 18 km as part of the Stratéole 2 campaign. A short test flight of the system was conducted from Laramie, Wyoming, in January 2021 to check the optical, electrical, and mechanical systems at altitude and to validate a four-reference temperature calibration procedure with a fiber-optic deployment length of 1170 m. During the 4 h flight aboard a vented balloon, FLOATS retrieved temperature profiles during ascent and while at a float altitude of about 19 km. The FLOATS retrievals provided differences of less than 1.0 ∘ C compared to a commercial radiosonde aboard the flight payload during ascent. At float altitude, a comparison of optical length and GPS position at the bottom of the fiber-optic revealed little to no curvature in the fiber-optic cable, suggesting that the position of any distributed temperature measurement can be effectively modeled. Comparisons of the distributed temperature retrievals to the reference temperature sensors show strong agreement with root-mean-square-error values less than 0.4 ∘ C. The instrument also demonstrated good agreement with nearby meteorological observations and COSMIC-2 satellite profiles. Observations of temperature and wind perturbations compared to the nearby radiosounding profiles provide evidence of inertial gravity wave activity during the test flight. Spectral analysis of the observed temperature perturbations shows that FLOATS is an effective and pioneering tool for the investigation of small-scale gravity waves in the upper troposphere and lower stratosphere. [ABSTRACT FROM AUTHOR]

Generating spatial crop yield information is of great significance for academic research and guiding agricultural policy. Most existing public yield datasets have a coarse spatial resolution. Although these datasets are useful for analyzing regional temporal and spatial change, they cannot deal with spatial heterogeneity, which happens to be the most significant characteristic of the Chinese small-scale farmers' economy. Hence, we generated a 30-m Chinese winter wheat yield dataset (ChinaWheatYield30m) for major winter wheat-producing provinces in China for the period 2016-2021 with a semi-mechanistic model (hierarchical linear model, HLM). The yield prediction model was built by considering the wheat growth status and climatic factors. It can estimate wheat yield with excellent accuracy and low cost using a combination of satellite observations and regional meteorological information (i.e., Landsat 8, Sentinel-2 and ERA5 data from the Google Earth Engine (GEE) platform). The results were validated by using in situ measurements and census statistics and indicated a stable performance of the HLM model based on calibration datasets across China, with r of 0.81** and nRMSE of 12.59%. With regards to validation, the ChinaWheatYield30m dataset was highly consistent with in situ measurement data and census data, indicated by r (nRMSE) of 0.72** (15.34%) and 0.73** (19.41%). The ChinaWheatYield30m is a sophisticated dataset with both high spatial resolution and convictive accuracy, such a dataset will provide basic knowledge of exquisite wheat yield distribution, which can be applied for many purposes including crop production modelling or regional climate evaluation. [ABSTRACT FROM AUTHOR]

The Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) is employed as an intercomparison tool for validating TROPOspheric Monitoring Instrument (TROPOMI) satellite NO 2 retrievals against high-resolution Airborne Prism EXperiment (APEX) remote sensing observations performed in June 2019 in the region of Antwerp, a major hotspot of NO 2 pollution in Europe. The model is first evaluated using meteorological and chemical observations in this area. Sensitivity simulations varying the model planetary layer boundary (PBL) parameterization were conducted for a 3 d period in June 2019, indicating a generally good performance of most parameterizations against meteorological data (namely ceilometer, surface meteorology, and balloon measurements), except for a moderate overestimation (∼ 1 m s -1) of near-surface wind speed. On average, all but one of the PBL schemes reproduce the surface NO 2 measurements at stations of the Belgian Interregional Environmental Agency fairly well, although surface NO 2 is generally underestimated during the day (between -4.3 % and -25.1 % on average) and overestimated at night (8.2 %–77.3 %). This discrepancy in the diurnal evolution arises despite (1) implementing a detailed representation of the diurnal cycle of emissions (Crippa et al., 2020) and (2) correcting the modeled concentrations to account for measurement interferences due to NO y reservoir species, which increases NO 2 concentrations by about 20 % during the day. The model is further evaluated by comparing a 15 d simulation with surface NO 2 , NO, CO, and O 3 data in the Antwerp region. The modeled daytime NO 2 concentrations are more negatively biased during weekdays than during weekends, indicating a misrepresentation of the weekly temporal profile applied to the emissions obtained from Crippa et al. (2020). Using a mass balance approach, we determined a new weekly profile of NO x emissions, leading to a homogenization of the relative bias among the different weekdays. The ratio of weekend to weekday emissions is significantly lower in this updated profile (0.6) than in the profile based on Crippa et al. (2020; 0.84). Comparisons with remote sensing observations generally show a good reproduction of the spatial patterns of NO 2 columns by the model. The model underestimated both APEX (by ca. -37 %) and TROPOMI columns (ca. -25 %) on 27 June, whereas no significant bias is found on 29 June. The two datasets are intercompared by using the model as an intermediate platform to account for differences in vertical sensitivity through the application of averaging kernels. The derived bias of TROPOMI v1.3.1 NO 2 with respect to APEX is about -10 % for columns between (6–12) × 10 15 molec. cm -2. The obtained bias for TROPOMI v1.3.1 increases with the NO 2 column, following CAPEX=1.217Cv1.3-0.783 × 10 15 molec. cm -2 , in line with previous validation campaigns. The bias is slightly lower for the reprocessed TROPOMI v2.3.1, with CAPEX=1.055CPAL-0.437 × 10 15 molec. cm -2 (PAL). Finally, a mass balance approach was used to perform a crude inversion of NO x emissions based on 15 d averaged TROPOMI columns. The emission correction is conducted only in regions with high columns and high sensitivity to emission changes in order to minimize the errors due to wind transport. The results suggest that emissions increase over Brussels–Antwerp (+20 %), the Ruhr Valley (13 %), and especially Paris (+39 %), and emissions decrease above a cluster of power plants in western Germany. [ABSTRACT FROM AUTHOR]

Visibility data are fundamental meteorological observation data widely used in many fields. When using visibility data, it is often necessary to calculate the average visibility, which used to be the arithmetic average of the visibility data directly. In this study, we first analyze the relationship between the visibility, the extinction coefficient, and the atmospheric compositions. Then we propose to use the harmonic average of visibility data as the average visibility, which can better reflect changes in atmospheric extinction coefficients and aerosol concentrations. It is recommended to use the harmonic average visibility in the studies of climate change, atmospheric radiation, air pollution, environmental health, etc. [ABSTRACT FROM AUTHOR]

The lightning assimilation (LTA) technique in the Kain–Fritsch convective parameterization in the Weather Research and Forecasting (WRF) model has been updated and applied to continental and hemispheric simulations using lightning flash data obtained from the National Lightning Detection Network (NLDN) and the World Wide Lightning Location Network (WWLLN), respectively. The LTA technique uses lightning data to trigger the Kain–Fritsch convective parameterization via realistic temperature and moisture perturbations. The impact of different values for cumulus parameters associated with the Kain–Fritsch scheme on simulations with and without LTA were evaluated for both the continental and the hemispheric simulations. Comparisons to gauge-based rainfall products and near-surface meteorological observations indicated that the LTA improved the model's performance for most variables. The simulated precipitation with LTA, using WWLLN lightning flashes in the hemispheric applications, was significantly improved over the simulations without LTA when compared to precipitation from satellite observations in the equatorial regions. The simulations without LTA showed significant sensitivity to the cumulus parameters (i.e., user-toggled switches) for monthly precipitation that was as large as 40 % during convective seasons for monthly mean daily precipitation. With LTA, the differences in simulated precipitation due to the different cumulus parameters were minimized. The horizontal grid spacing of the modeling domain strongly influenced the LTA technique and the predicted total precipitation, especially in the coarser scales used for the hemispheric simulation. The user-definable cumulus parameters and domain resolution manifested the complexity of convective process modeling both with and without LTA. These results revealed sensitivities to domain resolution, geographic heterogeneity, and the source and quality of the lightning dataset. [ABSTRACT FROM AUTHOR]

We evaluate the influence of a forest parametrization on the simulation of the boundary layer flow over moderate complex terrain in the context of the Perdigão 2017 field campaign. The numerical simulations are performed using the Weather Research and Forecasting model in large eddy simulation mode (WRF-LES). The short-term, high-resolution (40 m horizontal grid spacing) and long-term (200 m horizontal grid spacing) WRF-LES are evaluated for an integration time of 12 h and 1.5 months, respectively, with and without forest parameterization. The short-term simulations focus on low-level jet events over the valley, while the long-term simulations cover the whole intensive observation period (IOP) of the field campaign. The results are validated using lidar and meteorological tower observations. The mean diurnal cycle during the IOP shows a significant improvement of the along-valley wind speed and the wind direction when using the forest parametrization. However, the drag imposed by the parametrization results in an underestimation of the cross-valley wind speed, which can be attributed to a poor representation of the land surface characteristics. The evaluation of the high-resolution WRF-LES shows a positive influence of the forest parametrization on the simulated winds in the first 500 m above the surface. [ABSTRACT FROM AUTHOR]

A multitude of physical and biological processes on which ecosystems and human societies depend are governed by the climate, and understanding how these processes are altered by climate change is central to mitigation efforts. We developed a set of climate-related variables at as yet unprecedented spatiotemporal detail as a basis for environmental and ecological analyses. We downscaled time series of near-surface relative humidity (hurs) and cloud area fraction (clt) under the consideration of orography and wind as well as near-surface wind speed (sfcWind) using the delta-change method. Combining these grids with mechanistically downscaled information on temperature, precipitation, and solar radiation, we then calculated vapor pressure deficit (vpd), surface downwelling shortwave radiation (rsds), potential evapotranspiration (pet), the climate moisture index (cmi), and site water balance (swb) at a monthly temporal and 30 arcsec spatial resolution globally from 1980 until 2018 (time-series variables). At the same spatial resolution, we further estimated climatological normals of frost change frequency (fcf), snow cover days (scd), potential net primary productivity (npp), growing degree days (gdd), and growing season characteristics for the periods 1981–2010, 2011–2040, 2041–2070, and 2071–2100, considering three shared socioeconomic pathways (SSP126, SSP370, SSP585) and five Earth system models (projected variables). Time-series variables showed high accuracy when validated against observations from meteorological stations and when compared to alternative products. Projected variables were also highly correlated with observations, although some variables showed notable biases, e.g., snow cover days. Together, the CHELSA-BIOCLIM + dataset presented here (10.16904/envidat.332, Brun et al., 2022) allows improvement to our understanding of patterns and processes that are governed by climate, including the impact of recent and future climate changes on the world's ecosystems and the associated services on societies. [ABSTRACT FROM AUTHOR]

The changing Arctic climate is creating increased economic, transportation, and recreational activities requiring reliable and relevant weather information. However, the Canadian Arctic is sparsely observed, and processes governing weather systems in the Arctic are not well understood. There is a recognized lack of meteorological data to characterize the Arctic atmosphere for operational forecasting and to support process studies, satellite calibration/validation, search and rescue operations (which are increasing in the region), high-impact weather (HIW) detection and prediction, and numerical weather prediction (NWP) model verification and evaluation. To address this need, Environment and Climate Change Canada commissioned two supersites, one in Iqaluit (63.74 ∘ N, 68.51 ∘ W) in September 2015 and the other in Whitehorse (60.71 ∘ N, 135.07 ∘ W) in November 2017 as part of the Canadian Arctic Weather Science (CAWS) project. The primary goals of CAWS are to provide enhanced meteorological observations in the Canadian Arctic for HIW nowcasting (short-range forecast) and NWP model verification, evaluation, and process studies and to provide recommendations on the optimal cost-effective observing system for the Canadian Arctic. Both sites are in provincial/territorial capitals and are economic hubs for the region; they also act as transportation gateways to the north and are in the path of several common Arctic storm tracks. The supersites are located at or next to major airports and existing Meteorological Service of Canada ground-based weather stations that provide standard meteorological surface observations and upper-air radiosonde observations; they are also uniquely situated in close proximity to frequent overpasses by polar-orbiting satellites. The suite of in situ and remote sensing instruments at each site is completely automated (no on-site operator) and operates continuously in all weather conditions, providing near-real-time data to operational weather forecasters, the public, and researchers via obrs.ca. The two sites have similar instruments, including mobile Doppler weather radars, multiple vertically profiling and/or scanning lidars (Doppler, ceilometer, water vapour), optical disdrometers, precipitation gauges in different shielded configurations, present weather sensors, fog monitoring devices, radiation flux sensors, and other meteorological instruments. Details on the two supersites, the suites of instruments deployed, the data collection methods, and example case studies of HIW events are discussed. CAWS data are publicly accessible via the Canadian Government Open Data Portal (https://doi.org/10.18164/ff771396-b22c-4bc3-844d-38fc697049e9 , Mariani et al., 2022a, and https://doi.org/10.18164/d92ed3cf-4ba0-4473-beec-357ec45b0e78 , Mariani et al., 2022b); this dataset is being used to improve our understanding of synoptic and fine-scale meteorological processes in the Arctic and sub-Arctic, including HIW detection and prediction and NWP verification, assimilation, and processes. [ABSTRACT FROM AUTHOR]

Doppler wind lidars (DWLs) have increasingly been used over the last decade to derive the mean wind in the atmospheric boundary layer. DWLs allow the determination of wind vector profiles with high vertical resolution and provide an alternative to classic meteorological tower observations. They also receive signals from altitudes higher than a tower and can be set up flexibly in any power-supplied location. In this work, we address the question of whether and how wind gusts can be derived from DWL observations. The characterization of wind gusts is one central goal of the Field Experiment on Sub-Mesoscale Spatio-Temporal Variability in Lindenberg (FESSTVaL). Obtaining wind gusts from a DWL is not trivial because a monostatic DWL provides only a radial velocity per line of sight, i.e., only one component of a three-dimensional vector, and measurements in at least three linearly independent directions are required to derive the wind vector. Performing them sequentially limits the achievable time resolution, while wind gusts are short-lived phenomena. This study compares different DWL configurations in terms of their potential to derive wind gusts. For this purpose, we develop a new wind retrieval method that is applicable to different scanning configurations and various time resolutions. We test eight configurations with StreamLine DWL systems from HALO Photonics and evaluate gust peaks and mean wind over 10 min at 90 m a.g.l. against a sonic anemometer at the meteorological tower in Falkenberg, Germany. The best-performing configuration for retrieving wind gusts proves to be a fast continuous scanning mode (CSM) that completes a full observation cycle within 3.4 s. During this time interval, about 11 radial Doppler velocities are measured, which are then used to retrieve single gusts. The fast CSM configuration was successfully operated over a 3-month period in summer 2020. The CSM paired with our new retrieval technique provides gust peaks that compare well to classic sonic anemometer measurements from the meteorological tower. [ABSTRACT FROM AUTHOR]

A high time resolution synchronous sampling method al ong with determination of stable carbon isotopes of gaseous water-soluble organic carbon (WSOCg) and particulate water-soluble organic carbon (WSOCp) was realized in this research through equipment modification and method improvement. It was found that WSOCg has significant higher concentration than WSOCp and a more depleted δ13C. Both concentrations of WSOCp and WSOCg have the seasonal variation characteristics of high in winter (WSOCp=15.7±3.9µg/m³; WSOCg=42.4±6.0µg/m³) and low in summer (WSOCp=5.9±1.8µg/m³; WSOCg=25.2±5.2µg/m³), with greater increase in WSOCp (167%) than that in WSOCg (68%). During wintertime, WSOCp and WSOCg had similar daily variation characteristics of concentration, and opposite daily variation characteristics of δ13C. WSOCp had a bimodal distribution with obvious low value at sunrise and sunset, while δ13C WSOCp had a unimodal distribution with low in daytime (-24.6±1.1‰) and high in nighttime (- 22.3±1.7‰). WSOCg and δ13C-WSOCg had same distribution with high in daytime and (49.3±8.8µg/m³; -27.9±1.1‰) low in nighttime (38.3±4.6µg/m³; -29.9±0.4‰). Combining the δ13C variation characteristics with the synchronous observation results of meteorological conditions, gaseous precursor pollutants, gaseous oxidants, gaseous acids and fine particle components, the restriction factors of WSOC gas-particle distribution mechanism were discussed. The presence of radiation rather than its intensity decided whether generations process of WSOCp and WSOCg are divided, for δ13C-WSOC of two phases showed significant correlation only during daytime. Meteorological conditions, gaseous precursor pollutants, gaseous oxidants and gaseous acids restrict the gas particle distribution of WSOC by affecting the aging process of WSOCp and WSOCg, gas particle conversion ratio of semi-volatile organic compounds (SVOC) and the gas phase and liquid phase generation ratio of WSOCp. At the same time, the gas-particle distribution process of WSOC is strongly related to the formation of secondary inorganic ions (nitrate, sulfate, ammonium), and the gas-particle distribution between gaseous nitrous acid and nitrite. [ABSTRACT FROM AUTHOR]

The main in-situ data base for numerical weather prediction currently relies on radiosonde and airliner observations, with large data gaps above the oceans and in polar regions. These gaps might possibly be patched by measurements with drones, which provide a significant improvement towards environment friendly additional data, but so far have not been regarded as a feasible alternative of performing measurements in the upper troposphere. In this report, the development of a system that is capable of sounding the atmosphere under Antarctic and mid-European conditions is presented. After an assessment of the environmental conditions, the design of the system and the measurements are presented, including the process to get permissions for such flight tests in Europe. The feasibility of reaching an altitude of 10 km with a small meteorologically equipped drone is shown, and the first data are compared to radiosonde measurements. The article closes with an outlook on the potential use of drones for filling data gaps in the troposphere. [ABSTRACT FROM AUTHOR]

In this paper, we present a comprehensive experiment, namely, an Integrated Microwave Radiometry Campaign for snow (IMCS), in Xinjiang, China, during the snow season of 2015–2016. The campaign hosted a dual-polarized microwave radiometer operating at L, K, and Ka bands to provide minutely passive microwave observations of snow cover at a fixed site, along with daily manual snow pit observations of snow physical parameters, automatic observations of 10 min four-component radiation and layered snow temperatures, and meteorological observations of hourly weather data and soil data. To the best of our knowledge, our dataset is unique in providing continuous daily snow pit data and coincident microwave brightness temperatures, radiation and meteorological data, at a fixed site over a full season, which can be straightforwardly used for evaluation and development of microwave radiative transfer models and snow process models, along with land surface process and hydrology models. The consolidated data are available at (10.11888/Snow.tpdc.270886) (Dai, 2020). [ABSTRACT FROM AUTHOR]

The study provides estimates of summertime evaporation over a glacial lake located in the Schirmacher oasis, Dronning Maud Land, East Antarctica. Lake Zub (alternately named Lake Priyadarshini and referred to throughout as Lake Zub/Priyadarshini) is the second-largest lake in the oasis, and its maximum depth is 6 m. The lake is also among the warmest glacial lakes in the oasis, and it is free of ice during almost 2 summer months. The summertime evaporation over the ice-free lake was measured using the eddy covariance method and estimated on the basis of five indirect methods (bulk-aerodynamic method and four combination equations). We used meteorological and hydrological measurements collected during a field experiment carried out in 2018. The eddy covariance method was considered the most accurate, and the evaporation was estimated to be 114 mm for the period from 1 January to 7 February 2018 (38 d) on the basis of this method. The average daily evaporation was 3.0 mm d -1 in January 2018. During the experiment period, the largest changes in daily evaporation were driven by synoptic-scale atmospheric processes rather than local katabatic winds. The bulk-aerodynamic method suggests the average daily evaporation is 2.0 mm d -1 , which is 32 % less than the results based on the eddy covariance method. The bulk-aerodynamic method is much better in producing the day-to-day variations in evaporation compared to the combination equations. All selected combination equations underestimated the evaporation over the lake by 40 %–72 %. The scope of the uncertainties inherent in the indirect methods does not allow us to apply them to estimate the daily evaporation over Lake Zub/Priyadarshini. We suggested a new combination equation to evaluate the summertime evaporation over the lake's surface using meteorological observations from the nearest site. The performance of the new equation is better than the performance of the indirect methods considered. With this equation, the evaporation over the period of the experiment was 124 mm, which is only 9 % larger than the result according to the eddy covariance method. [ABSTRACT FROM AUTHOR]

An accurate spatially continuous air temperature data set is crucial for multiple applications in the environmental and ecological sciences. Existing spatial interpolation methods have relatively low accuracy, and the resolution of available long-term gridded products of air temperature for China is coarse. Point observations from meteorological stations can provide long-term air temperature data series but cannot represent spatially continuous information. Here, we devised a method for spatial interpolation of air temperature data from meteorological stations based on powerful machine learning tools. First, to determine the optimal method for interpolation of air temperature data, we employed three machine learning models: random forest, support vector machine, and Gaussian process regression. A comparison of the mean absolute error, root mean square error, coefficient of determination, and residuals revealed that a Gaussian process regression had high accuracy and clearly outperformed the other two models regarding the interpolation of monthly maximum, minimum, and mean air temperatures. The machine learning methods were compared with three traditional methods used frequently for spatial interpolation: inverse distance weighting, ordinary kriging, and ANUSPLIN (Australian National University Spline). Results showed that the Gaussian process regression model had higher accuracy and greater robustness than the traditional methods regarding interpolation of monthly maximum, minimum, and mean air temperatures in each month. A comparison with the TerraClimate (Monthly Climate and Climatic Water Balance for Global Terrestrial Surfaces), FLDAS (Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System), and ERA5 (ECMWF, European Centre for Medium-Range Weather Forecasts, Climate Reanalysis) data sets revealed that the accuracy of the temperature data generated using the Gaussian process regression model was higher. Finally, using the Gaussian process regression method, we produced a long-term (January 1951 to December 2020) gridded monthly air temperature data set, with 1 km resolution and high accuracy for China, which we named GPRChinaTemp1km. The data set consists of three variables: monthly mean air temperature, monthly maximum air temperature, and monthly minimum air temperature. The obtained GPRChinaTemp1km data were used to analyse the spatiotemporal variations of air temperature using Theil–Sen median trend analysis in combination with the Mann–Kendall test. It was found that the monthly mean and minimum air temperatures across China were characterised by a significant trend of increase in each month, whereas monthly maximum air temperatures showed a more spatially heterogeneous pattern, with significant increase, non-significant increase, and non-significant decrease. The GPRChinaTemp1km data set is publicly available at 10.5281/zenodo.5112122 (He et al., 2021a) for monthly maximum air temperature, at 10.5281/zenodo.5111989 (He et al., 2021b) for monthly mean air temperature, and at 10.5281/zenodo.5112232 (He et al., 2021c) for monthly minimum air temperature. [ABSTRACT FROM AUTHOR]

In this paper we present EMO-5 ("European Meteorological Observations", spatial resolution of 5 km), a European high-resolution, (sub-)daily, multi-variable meteorological dataset built on historical and real-time observations obtained by integrating data from 18 964 ground weather stations, four high-resolution regional observational grids (i.e. CombiPrecip, ZAMG – INCA, EURO4M-APGD, and CarpatClim), and one global reanalysis (ERA-Interim/Land). EMO-5 includes the following at daily resolution: total precipitation, temperatures (minimum and maximum), wind speed, solar radiation, and water vapour pressure. In addition, EMO-5 also makes available 6-hourly precipitation and mean temperature data. The raw observations from the ground weather stations underwent a set of quality controls before SPHEREMAP and Yamamoto interpolation methods were applied in order to estimate for each 5×5 km grid cell the variable value and its affiliated uncertainty, respectively. The quality of the EMO-5 precipitation data was evaluated through (1) comparison with two regional high-resolution datasets (i.e. seNorge2 and seNorge2018), (2) analysis of 15 heavy precipitation events, and (3) examination of the interpolation uncertainty. Results show that EMO-5 successfully captured 80 % of the heavy precipitation events, and that it is of comparable quality to a regional high-resolution dataset. The availability of the uncertainty fields increases the transparency of the dataset and hence the possible usage. EMO-5 (version 1) covers the time period from 1990 to 2019, with a near real-time release of the latest gridded observations foreseen with version 2. As a product of Copernicus, the EU's Earth Observation Programme, the EMO-5 dataset is free and open, and can be accessed at 10.2905/0BD84BE4-CEC8-4180-97A6-8B3ADAAC4D26 (Thiemig et al., 2020). [ABSTRACT FROM AUTHOR]

Fine particulate matter (PM 2.5) has altered the radiation balance on Earth and raised environmental and health risks for decades but has only been monitored widely since 2013 in China. Historical long-term PM 2.5 records with high temporal resolution are essential but lacking for both research and environmental management. Here, we reconstruct a site-based PM 2.5 dataset at 6 h intervals from 1960 to 2020 that combines long-term visibility, conventional meteorological observations, emissions, and elevation. The PM 2.5 concentration at each site is estimated based on an advanced machine learning model, LightGBM, that takes advantage of spatial features from 20 surrounding meteorological stations. Our model's performance is comparable to or even better than those of previous studies in by-year cross validation (CV) (R2=0.7) and spatial CV (R2=0.76) and is more advantageous in long-term records and high temporal resolution. This model also reconstructs a 0.25 ∘ × 0.25 ∘ , 6-hourly, gridded PM 2.5 dataset by incorporating spatial features. The results show PM 2.5 pollution worsens gradually or maintains before 2010 from an interdecadal scale but mitigates in the following decade. Although the turning points vary in different regions, PM 2.5 mass concentrations in key regions decreased significantly after 2013 due to clean air actions. In particular, the annual average value of PM 2.5 in 2020 is nearly the lowest since 1960. These two PM 2.5 datasets (publicly available at 10.5281/zenodo.6372847, Zhong et al., 2022) provide spatiotemporal variations at high resolution, which lay the foundation for research studies associated with air pollution, climate change, and atmospheric chemical reanalysis. [ABSTRACT FROM AUTHOR]

A multitude of physical and biological processes on which ecosystems and human societies depend are governed by climatic conditions. Understanding how these processes are altered by climate change is central to mitigation efforts. Based on mechanistically downscaled climate data, we developed a set of climate-related variables at yet unprecedented spatiotemporal detail as a basis for environmental and ecological analyses. We created gridded data for near-surface relative humidity (hurs), cloud area fraction (clt), near-surface wind speed (sfcWind), vapour pressure deficit (vpd), surface downwelling shortwave radiation (rsds), potential evapotranspiration (pet), climate moisture index (cmi), and site water balance (swb), at a monthly temporal and 30 arcsec spatial resolution globally, from 1980 until 2018 (time-series variables). At the same spatial resolution, we further estimated climatological normals of frost change frequency (fcf), snow cover days (scd), potential net primary productivity (npp), growing degree days (gdd), and growing season characteristics for the periods 1981-2010, 2011-2040, 2041-2070, and 2071-2100, considering three shared socioeconomic pathways (SSP126, SSP370, SSP585) and five Earth system models (projected variables). Time-series variables showed high accuracy when validated against observations from meteorological stations. Projected variables were also highly correlated to observations, although some variables showed notable biases, e.g., snow cover days (scd). Together, the CHELSA-BIOCLIM+ data set presented here (https://doi.org/10.16904/envidat.332, Brun et al., 2022) allows improving our understanding of patterns and processes that are governed by climate, including the impact of recent and future climate changes on the world's ecosystems and associated services to societies. [ABSTRACT FROM AUTHOR]

The changing Arctic climate is creating increased economic, transportation, and recreational activities requiring reliable and relevant weather information. However, the Canadian Arctic is sparsely observed and processes governing weather systems in the Arctic are not well understood. There is a recognized lack of meteorological data to characterize the Arctic atmosphere for operational forecasting and to support process studies, satellite calibration/validation, search and rescue operations (which are increasing in the region), high impact weather (HIW) detection and prediction, and numerical weather prediction (NWP) model verification and evaluation. To address this need, Environment and Climate Change Canada commissioned two supersites; one in Iqaluit (63.74°N, 68.51°W) in September 2015 and the other in Whitehorse (60.71°N, 135.07°W) in November 2017 as part of the Canadian Arctic Weather Science (CAWS) project. The primary goals of CAWS are to provide enhanced meteorological observations in the Canadian Arctic for HIW nowcasting (short-range forecast) and NWP model verification, evaluation, and process studies, and to provide recommendations on the optimal cost-effective observing system for the Canadian Arctic. Both sites are in Provincial/Territorial capitals and are economic hubs for the region; they also act as transportation gateways to the North and are in the path of several common Arctic storm tracks. The supersites are located at or next to major airports and existing Meteorological Service of Canada ground-based weather stations that provide standard meteorological surface observations and upper air radiosonde observations; they are also uniquely situated in close proximity to frequent overpasses by polar-orbiting satellites. The suite of in-situ and remote sensing instruments at each site are completely automated (no on-site operator) and operate continuously in all weather conditions, providing nearreal time data to operational weather forecasters, the public, and researchers via obrs.ca. The two sites have similar instruments, including mobile Doppler weather radars, multiple vertically-profiling and/or scanning lidars (Doppler, ceilometer, water vapour), optical disdrometers, precipitation gauges in different shielded configurations, present weather sensors, fog monitoring devices, radiation flux sensors, and other meteorological instruments. Details on the two supersites, the suites of instruments deployed, data collection methods, and example case studies of HIW events are discussed. CAWS data are publically accessible via the Canadian Government Open Data Portal (https://doi.org/10.18164/ff771396-b22c-4bc3-844dhttps:// 38fc697049e9 (Mariani et al., 2022a) and https://doi.org/10.18164/d92ed3cf-4ba0-4473-beec-357ec45b0e78 (Mariani et al., 2022b)); this dataset is being used to improve our understanding of synoptic and fine-scale meteorological processes in the Arctic and sub-Arctic, including HIW detection and prediction and NWP verification, assimilation, and processes. [ABSTRACT FROM AUTHOR]

This article presents a comprehensive hydrometeorological dataset collected over the past two decades throughout the Cordillera Blanca, Peru. The data-recording sites, located in the upper portion of the Rio Santa valley, also known as the Callejon de Huaylas, span an elevation range of 3738–4750 m a.s.l. As many historical hydrological stations measuring daily discharge across the region became defunct after their installation in the 1950s, there was a need for new stations to be installed and an opportunity to increase the temporal resolution of the streamflow observations. Through inter-institutional collaboration, the hydrometeorological network described in this paper was deployed with the goal of evaluating how progressive glacier mass loss was impacting stream hydrology, and understanding better the local manifestation of climate change over diurnal to seasonal and interannual time scales. The four automatic weather stations supply detailed meteorological observations and are situated in a variety of mountain landscapes, with one on a high-mountain pass, another next to a glacial lake, and two in glacially carved valleys. Four additional temperature and relative humidity loggers complement the weather stations within the Llanganuco valley by providing these data across an elevation gradient. The six streamflow gauges are located in tributaries to the Rio Santa and collect high-temporal-resolution runoff data. The datasets presented here are available freely from 10.4211/hs.35a670e6c5824ff89b3b74fe45ca90e0 (Mateo et al., 2021). Combined, the hydrological and meteorological data collected throughout the Cordillera Blanca enable detailed research of atmospheric and hydrological processes in tropical high-mountain terrain. [ABSTRACT FROM AUTHOR]

This study presents the first-ever complete characterization of random errors in dual-polarimetric spectral observations of meteorological targets by cloud radars. The characterization is given by means of mathematical equations for joint probability density functions (PDFs) and error covariance matrices. The derived equations are checked for consistency using real radar measurements. One of the main conclusions of the study is that the convenient representation of spectral polarimetric measurements including differential reflectivity ZDR , correlation coefficient ρHV , and differential phase ΦDP is not suited for the proper characterization of the error covariance matrix. This is because the aforementioned quantities are complex, non-linear functions of the radar raw data, and thus their error covariance matrix is commonly derived using simplified linear relations and by neglecting the correlation of errors. This study formulates the spectral polarimetric measurements in terms of a different set of quantities that allows for a proper analytic treatment of their error covariance matrix. The results given in this study allow for utilization of spectral polarimetric measurements for advanced meteorological applications, among which are variational retrieval techniques, data assimilation, and sensitivity analysis. [ABSTRACT FROM AUTHOR]

The warming of the Arctic climate is well documented, but the mechanisms of Arctic amplification are still not fully understood. Thus, monitoring of glaciological and meteorological variables and the environmental response to accelerated climate warming must be continued and developed in Svalbard. Long-term meteorological observations carried out in situ on glaciers in conjunction with glaciological monitoring are rare in the Arctic and significantly expand our knowledge about processes in the polar environment. This study presents glaciological and meteorological data collected for 2009–2020 in southern Spitsbergen (Werenskioldbreen). The meteorological data are composed of air temperature, relative humidity, wind speed, short-wave and long-wave upwelling and downwelling radiation on 10 min, hourly and daily resolution (2009–2020). The snow dataset includes 49 data records from 2009 to 2019 with the snow depth, snow bulk density and snow water equivalent data. The glaciological data consist of seasonal and annual surface mass balance measurements (point and glacier-wide) for 2009–2020. The paper also includes modelling of the daily glacier surface ablation (2009–2020) based on the presented data. The datasets are expected to serve as local forcing data in hydrological and glaciological models as well as validation of calibration of remote sensing products. The datasets are available from the Polish Polar Database (https://ppdb.us.edu.pl/ , last access: 24 May 2022) and Zenodo (10.5281/zenodo.6528321, Ignatiuk, 2021a; 10.5281/zenodo.5792168, Ignatiuk, 2021b). [ABSTRACT FROM AUTHOR]

The 26 km long tidewater glacier Tunabreen is the most frequently surging glacier in Svalbard, with four documented surges in the past 100 years. We model the evolution of this glacier with a minimal glacier model (MGM), in which ice mechanics, calving, and surging are parameterized. The model geometry consists of a flow band to which three tributaries supply mass. The calving rate is set to the mean observed value for the period 2012–2019 and kept constant. For the past 120 years, a smooth equilibrium line altitude (ELA) history is reconstructed by finding the best possible match between observed and simulated glacier length. There is a modest correlation between this reconstructed ELA history and an ELA history based on meteorological observations from Longyearbyen. Runs with and without surging show that the effect of surging on the long-term glacier evolution is limited. Due to the low surface slope and associated strong height–mass-balance feedback, Tunabreen is very sensitive to changes in the ELA. For a constant future ELA equal to the reconstructed value for 2020, the glacier front will retreat by 8 km during the coming 100 years. For an increase in the ELA of 2 m a -1 , the retreat is projected to be 13 km, and Tunabreen becomes a land-terminating glacier around 2100. The calving parameter is an important quantity: increasing its value by 50 % has about the same effect as a 35 m increase in the ELA, with the corresponding equilibrium glacier length being 17.5 km (as compared to 25.8 km in the reference state). Response times vary from 150 to 400 years, depending on the forcing and on the state of the glacier (tidewater or land-terminating). [ABSTRACT FROM AUTHOR]

Fine particulate matter (PM2.5) has altered radiation balance on earth and raised environmental and health risks for decades, but only been monitored widely since 2013 in China. Historical long-term PM2.5 records with high temporal resolution are essential but lacking for both research and environmental management. Here, we reconstruct a site-based PM2.5 dataset at 6-hour intervals from 1960 to 2020 that combines long-term visibility, conventional meteorological observations, emissions, and elevation. The PM2.5 concentration at each site is estimated based on an advanced machine learning model, LightGBM, that takes advantage of spatial features from 20 surrounding meteorological stations. Our model's performance is comparable or even better than those of previous studies in by-year cross validation (CV) (R2=0.7) and spatial CV (R²=0.76), and more advantageous in long-term records and high temporal resolution. This model also reconstructs a 0.25°×0.25°, 6-hourly, gridded PM2.5 dataset by incorporating spatial features. The results show PM2.5 pollution worsens gradually or maintains before 2010 from an interdecadal scale but mitigates in the following decade. Although the turning points vary in different regions, PM2.5 mass concentrations in key regions decreased significantly after 2013 due to clean air actions. In particular, the annual average value of PM2.5 in 2020 is nearly at the lowest value in history since 1960. These two PM2.5 datasets (publicly available at https://doi.org/10.5281/zenodo.6372847) provide spatiotemporal variations at high resolution, which lay the foundation of research studies associated with air pollution, climate change, and atmospheric chemical reanalysis. [ABSTRACT FROM AUTHOR]

This work introduces the S2M (SAFRAN–SURFEX/ISBA–Crocus–MEPRA) meteorological and snow cover reanalysis in the French Alps, Pyrenees and Corsica, spanning the time period from 1958 to 2021. The simulations are made over elementary areas, referred to as massifs, designed to represent the main drivers of the spatial variability observed in mountain ranges (elevation, slope and aspect). The meteorological reanalysis is performed by the SAFRAN system, which combines information from numerical weather prediction models (ERA-40 reanalysis from 1958 to 2002, ARPEGE from 2002 to 2021) and the best possible set of available in situ meteorological observations. SAFRAN outputs are used to drive the Crocus detailed snow cover model, which is part of the land surface scheme SURFEX/ISBA. This model chain provides simulations of the evolution of the snow cover, underlying ground and the associated avalanche hazard using the MEPRA model. This contribution describes and discusses the main climatological characteristics (climatology, variability and trends) and the main limitations of this dataset. We provide a short overview of the scientific applications using this reanalysis in various scientific fields related to meteorological conditions and the snow cover in mountain areas. An evaluation of the skill of S2M is also displayed, in particular through comparison to 665 independent in situ snow depth observations. Further, we describe the technical handling of this open-access dataset, available at https://doi.org/10.25326/37#v2020.2. The S2M data are provided by Météo-France – CNRS, CNRM, Centre d'Études de la Neige, through AERIS. [ABSTRACT FROM AUTHOR]

The mountainous snow cover is highly variable at all temporal and spatial scales. Snowpack models only imperfectly represent this variability, because of uncertain meteorological inputs, physical parameterizations, and unresolved terrain features. In situ observations of the height of snow (HS), despite their limited representativeness, could help constrain intermediate and large-scale modeling errors by means of data assimilation. In this work, we assimilate HS observations from an in situ network of 295 stations covering the French Alps, Pyrenees, and Andorra, over the period 2009–2019. In view of assimilating such observations into a spatialized snow cover modeling framework, we investigate whether such observations can be used to correct neighboring snowpack simulations. We use CrocO, an ensemble data assimilation framework of snow cover modeling, based on a particle filter suited to the propagation of information from observed to unobserved areas. This ensemble system already benefits from meteorological observations, assimilated within SAFRAN analysis scheme. CrocO also proposes various localization strategies to assimilate snow observations. These approaches are evaluated in a leave-one-out setup against the operational deterministic model and its ensemble open-loop counterpart, both running without HS assimilation. Results show that an intermediate localization radius of 35–50 km yields a slightly lower root mean square error (RMSE), and a better spread–skill than the strategy of assimilating all the observations from a whole mountain range. Significant continuous ranked probability score (CRPS) improvements of about 13 % are obtained in the areas where the open-loop modeling errors are the largest, e.g., the Haute-Ariège, Andorra, and the extreme southern Alps. Over these areas, weather station observations are generally sparser, resulting in more uncertain meteorological analyses and, therefore, snow simulations. In situ HS observations thus show an interesting complementarity with meteorological observations to better constrain snow cover simulations over large areas. [ABSTRACT FROM AUTHOR]

Doppler wind lidars (DWLs) have increasingly been used over the last decade to derive the mean wind in the atmospheric boundary layer. DWLs allow the determination of wind vector profiles with high vertical resolution and provide an alternative to classic meteorological tower observations. They also receive signals from higher altitudes than a tower and can be set up flexibly in any power-supplied location. In this work, we address the question of whether and how wind gusts can be derived from DWL observations. The characterization of wind gusts is one central goal of the Field Experiment on Sub-Mesoscale Spatio-Temporal Variability in Lindenberg (FESSTVaL). Obtaining wind gusts from a DWL is not trivial because a monostatic DWL provides only a radial velocity per line-of-sight, i.e., only one component of a three-dimensional vector, and measurements in at least three linearly independent directions are required to derive the wind vector. Performing them sequentially limits the achievable time resolution, while wind gusts are short-lived phenomena. This study compares different DWL configurations in terms of their potential to derive wind gusts. For this purpose, we develop a new wind retrieval method that is applicable to different scanning configurations and various time resolutions. We test eight configurations with StreamLine DWL systems from Halo Photonics and evaluate gust peaks and mean wind over 10 minutes at 90 m a.g.l. against a sonic anemometer at the meteorological tower in Falkenberg, Germany. The best performing configuration for retrieving wind gusts proves to be a fast continuous scanning mode (CSM) that completes a full observation cycle within 3.4 s. During this time interval, about eleven radial Doppler velocities are measured, which are then used to retrieve single gusts. The fast CSM configuration was successfully operated over a three-month period in summer 2020. The CSM paired with our new retrieval technique provides gust peaks that compare well to classic sonic anemometer measurements from the meteorological tower. [ABSTRACT FROM AUTHOR]

The warming of the Arctic climate is well documented, but the mechanisms of Arctic amplification are still not fully understood. Thus, monitoring of glaciological and meteorological variables and the environmental response to accelerated climate warming must be continued and developed in Svalbard. Long-term meteorological observations carried out in situ on glaciers in conjunction with glaciological monitoring are rare in the Arctic and significantly expand our knowledge about processes in the polar environment. This study presents the unique glaciological and meteorological data collected in 2009-2020 in southern Spitsbergen (Werenskioldbreen). The meteorological data are comprised of air temperature, relative humidity, wind speed and direction, shortwave and longwave upwelling and downwelling radiation on 10 minutes, hourly and daily timescale (2009-2020). The snow dataset includes 49 sampling points from 2009-2019 with the snow depth, snow bulk density and SWE data. The glaciological data consist of point and surface annual winter, summer and net balance for 2009-2020. The paper also includes modelling of the daily glacier surface ablation (2009-2020) based on the presented data. The high-quality and long-term datasets are expected to serve as accurate forcing data in hydrological and glaciological models and validation of remote sensing products. The datasets are available from the and Polish Polar Database (https://ppdb.us.edu.pl/) and Zenodo (https://doi.org/10.5281/zenodo.5791748, Ignatiuk, 2021a; https://doi.org/10.5281/zenodo.5792168, Ignatiuk, 2021b). [ABSTRACT FROM AUTHOR]

An accurate spatially continuous air temperature dataset is crucial for multiple applications in environmental and ecological sciences. Existing spatial interpolation methods have relatively low accuracy and the resolution of available long-term gridded products of air temperature for China is coarse. Point observations from meteorological stations can provide long-term air temperature data series but cannot represent spatially continuous information. Here, we devised a method for spatial interpolation of air temperature data from meteorological stations based on powerful machine learning tools. First, to determine the optimal method for interpolation of air temperature data, we employed three machine learning models: random forest, support vector machine, and Gaussian process regression. Comparison of the mean absolute error, root mean square error, coefficient of determination, and residuals revealed that Gaussian process regression had high accuracy and clearly outperformed the other two models regarding interpolation of monthly maximum, minimum, and mean air temperatures. The machine learning methods were compared with three traditional methods used frequently for spatial interpolation: inverse distance weighting, ordinary kriging, and ANUSPLIN. Results showed that the Gaussian process regression model had higher accuracy and greater robustness than the traditional methods regarding interpolation of monthly maximum, minimum, and mean air temperatures in each month. Comparison with the TerraClimate, FLDAS, and ERA5 datasets revealed that the accuracy of the temperature data generated using the Gaussian process regression model was higher. Finally, using the Gaussian process regression method, we produced a long-term (January 1951 to December 2020) gridded monthly air temperature dataset with 1 km resolution and high accuracy for China, which we named GPRChinaTemp1km. The dataset consists of three variables: monthly mean air temperature, monthly maximum air temperature, and monthly minimum air temperature. The obtained GPRChinaTemp1km data were used to analyse the spatiotemporal variations of air temperature using Theil-Sen median trend analysis in combination with the Mann-Kendall test. It was found that the monthly mean and minimum air temperatures across China were characterized by a significant trend of increase in each month, whereas monthly maximum air temperature showed a more spatially heterogeneous pattern with significant increase, non-significant increase, and non-significant decrease. The GPRChinaTemp1km dataset is publicly available at https://doi.org/10.5281/zenodo.5112122 (He et al., 2021a) for monthly maximum air temperature, at https://doi.org/10.5281/zenodo.5111989 (He et al., 2021b) for monthly mean air temperature and at https://doi.org/10.5281/zenodo.5112232 (He et al., 2021c) for monthly minimum air temperature. [ABSTRACT FROM AUTHOR]

In this paper we present EMO- 51, a European high-resolution, (sub-)daily, multi-variable meteorological data set built on historical and real-time observations obtained by integrating data from 18,964 ground weather stations, four high-resolution regional observational grids (i.e. CombiPrecip, ZAMG - INCA, EURO4M-APGD and CarpatClim) as well as one global reanalysis (ERA-Interim/Land). EMO-5 includes at daily resolution: total precipitation, temperatures (mean, minimum and maximum), wind speed, solar radiation and water vapour pressure. In addition, EMO-5 also makes available 6-hourly precipitation and mean temperature. The raw observations from the ground weather stations underwent a set of quality controls, before SPHEREMAP and Yamamoto interpolation methods were applied in order to estimate for each 5 x 5 km grid cell the variable value and its affiliated uncertainty, respectively. The quality of the EMO-5 precipitation data was evaluated through (1) comparison with two regional high resolution data sets (i.e. seNorge2 and seNorge2018), (2) analysis of 15 heavy precipitation events, and (3) examination of the interpolation uncertainty. Results show that EMO-5 successfully captured 80 % of the heavy precipitation events, and that it is of comparable quality to a regional high resolution data set. The availability of the uncertainty fields increases the transparency of the data set and hence the possible usage. EMO-5 (release 1) covers the time period from 1990 to 2019, with a near real-time release of the latest gridded observations foreseen soon. As a product of Copernicus, the EU's Earth observation programme, EMO-5 dataset is free and open, and can be accessed at https://doi.org/10.2905/ 0BD84BE4-CEC8-4180-97A6-8B3ADAAC4D26 (Thiemig et al., 2021).1 EMO stands for 'European Meteorological Observations', whereas the 5 denotes the spatial resolution of 5 km. [ABSTRACT FROM AUTHOR]

Deriving large-scale and high-quality precipitation products from satellite remote-sensing spectral data is always challenging in quantitative precipitation estimation (QPE), and limited studies have been conducted even using China's latest Fengyun-4A (FY-4A) geostationary satellite. Taking three rainstorm events over South China as examples, a machine-learning-based regression model was established using the random forest (RF) method to derive QPE from FY-4A observations, in conjunction with cloud parameters and physical quantities. The cross-validation results indicate that both daytime (DQPE) and nighttime (NQPE) RF algorithms performed well in estimating QPE, with the bias score, correlation coefficient and root-mean-square error of DQPE (NQPE) of 2.17 (2.42), 0.79 (0.83) and 1.77 mmh-1 (2.31 mmh-1), respectively. Overall, the algorithm has a high accuracy in estimating precipitation under the heavy-rain level or below. Nevertheless, the positive bias still implies an overestimation of precipitation by the QPE algorithm, in addition to certain misjudgements from non-precipitation pixels to precipitation events. Also, the QPE algorithm tends to underestimate the precipitation at the rainstorm or even above levels. Compared to single-sensor algorithms, the developed QPE algorithm can better capture the spatial distribution of land-surface precipitation, especially the centre of strong precipitation. Marginal difference between the data accuracy over sites in urban and rural areas indicate that the model performs well over space and has no evident dependence on landscape. In general, our proposed FY-4A QPE algorithm has advantages for quantitative estimation of summer precipitation over East Asia. [ABSTRACT FROM AUTHOR]

Century-long continuous daily observations from some stations are important for the study of long-term trends and extreme climate events in the past. In this paper, three daily data sources – (1) the Department of Industry Agency of the British Concession in Tianjin covering 1 September 1890–31 December 1931, (2) the Water Conservancy Commission of North China covering 1 January 1932–31 December 1950 and (3) monthly journal sheets for Tianjin surface meteorological observation records covering 1 January 1951–31 December 2019 – have been collected from the Tianjin Meteorological Archive. The completed daily maximum and minimum temperature series for Tianjin from 1 January 1887 (1 September 1890 for minimum) to 31 December 2019 has been constructed and assessed for quality control with an early extension from 1890 back to 1887. Several significant breakpoints are detected by the penalized maximal T test (PMT) for the daily maximum and minimum time series using multiple reference series around Tianjin from monthly Berkeley Earth (BE), Climatic Research Unit Time-Series version 4.03 (CRU TS4.03) and Global Historical Climatology Network (GHCN) v3 data. Using neighboring daily series the record has been homogenized with quantile matching (QM) adjustments. Based on the homogenized dataset, the warming trend in annual mean temperature in Tianjin averaged from the newly constructed daily maximum and minimum temperature is evaluated as 0.154 ± 0.013 ∘ C per decade during the last 130 years. Trends of temperature extremes in Tianjin are all significant at the 5 % level and have much more coincident change than those from the raw data, with amplitudes of -1.454 , 1.196, -0.140 and 0.975 d per decade for cold nights (TN10p), warm nights (TN90p), cold days (TX10p) and warm days (TX90p) at the annual scale. The adjusted daily maximum, minimum and mean surface air temperature dataset for Tianjin city presented here is publicly available at 10.1594/PANGAEA.924561 (Si and Li, 2020). [ABSTRACT FROM AUTHOR]