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In the context of satellite altimetry, numerical weather models (NWMs) are relevant sources to derive the wet path delay (WPD) caused by the water vapor and cloud liquid water content in the atmosphere. Over regions where WPD based on observations are not available, these may be the only source of information. This article presents an assessment of the state-of-the-art NWMs from the European Centre for Medium-Range Weather Forecasts (ECMWF), the operational model (ECMWF OP), and the latest reanalysis (ERA5), for estimating the WPD of satellite altimeter observations, with focus on the direct impact of using different models on the estimation of altimeter-derived sea-level trends. Results show that, due to regular model updates, ECMWF OP possesses significant discontinuities that induce errors in sea-level trend which, in some regions, can exceed 1 mm yr−1 in absolute value, over periods of 7–8 years. The major impacts occur over the periods when Jason-2 and Jason-3 are in the reference orbit (phase A). Since this is the only model adopted in most altimeter products, it is strongly recommended that the most recent reanalysis model (currently ERA5) is included. Overall, ERA5 is the best compromise between model accuracy and stability, for sea/water-level studies and generally all climate applications, over regions where WPD based on observations is not available, such as some coastal and inland water regions.

Glaciers and snow are critical components of the hydrological cycle in the Himalayan region, and they play a vital role in river runoff. Therefore, it is crucial to monitor the glaciers and snow cover on a spatiotemporal basis to better understand the changes in their dynamics and their impact on river runoff. A significant amount of data is necessary to comprehend the dynamics of snow. Yet, the absence of weather stations in inaccessible locations and high elevation present multiple challenges for researchers through field surveys. However, the advancements made in remote sensing have become an effective tool for studying snow. In this article, the snow cover area (SCA) was analysed over the Beas River basin, Western Himalayas for the period 2003 to 2018. Moreover, its sensitivity towards temperature and precipitation was also analysed. To perform the analysis, two datasets, i.e., MODIS-based MOYDGL06 products for SCA estimation and the European Centre for Medium-Range Weather Forecasts (ECMWF) Atmospheric Reanalysis of the Global Climate (ERA5) for climate data were utilized. Results showed an average SCA of ~56% of its total area, with the highest annual SCA recorded in 2014 at ~61.84%. Conversely, the lowest annual SCA occurred in 2016, reaching ~49.2%. Notably, fluctuations in SCA are highly influenced by temperature, as evidenced by the strong connection between annual and seasonal SCA and temperature. The present study findings can have significant applications in fields such as water resource management, climate studies, and disaster management. [ABSTRACT FROM AUTHOR]

High spatiotemporal resolution atmospheric water vapor can be retrieved using the Global Navigation Satellite System (GNSS) tomography technique, in which the remained ill-posed problem of the tomography system resulting from the acquisition geometry is a vital issue to be addressed. Remote sensing (RS) water vapor data, with high-resolution and global coverage, show great potential for retrieval of slant water vapor (SWV) observations to improve the tomographic geometrical distribution. In this article, we develop a GNSS-RS (GNSS combining RS) tomography model to fully exploit the value of observation signals from GNSS and RS measurements. The two key factors of retrieving the RS SWV are performed by calibrating the original precipitable water vapor (PWV) images and adding the tropospheric horizontal gradients. The results reveal that when introducing the RS SWV observations into the tomography model, the acquisition geometry is significantly improved, with the average rate of voxels crossed by rays from 62% to 95% and the mean number of observation signals from 395 to 508 during the tomographic periods. Independent radiosonde data are used to validate the tomographic water vapor fields. The mean root-mean-square error (RMSE) and bias of the water vapor profiles derived from GNSS-RS solutions are decreased by 28% and 45% with respect to the GNSS-only results, respectively. Such improvements highlight that GNSS-RS troposphere tomography has significant potential to improve the reconstruction of the atmospheric water vapor fields. [ABSTRACT FROM AUTHOR]

An optimal interpolation (OI)-based technique is utilized to generate global daily gridded wind products from Scatsat-1 data. The first guess (i.e., background) for the OI is used from the analyses of National Centre for Medium Range Weather Forecasting (NCMRWF) global operational model. The generated gridded products are subsequently validated using observations from global moored buoys and surface winds analyses from European Centre for Medium-Range Weather Forecasts (ECMWF), and that shows a root mean square error of ~1.3 m/s in wind speed and ~19° in wind direction. Along with the gridded winds, several wind-dependent products (e.g., wind stress, wind divergence, wind stress curl, sensible and latent heat flux, etc.) are also generated. The gridded products from Scatsat-1 are available freely from Meteorological and Oceanographic Satellite Data Archival Center (http://www.mosdac.gov.in) since October 03, 2016. [ABSTRACT FROM AUTHOR]

Zhejiang province in China experienced an extreme climate phenomenon in August 2014 with temperature rises, sunshine duration decreases, and precipitation increases, particularly, the successive heavy rainfall events occurring from 16 to 20 August 2014 that contributed to this climate anomaly. This study investigates the spatial-temporal variation characteristics of precipitable water vapor (PWV) and the normalized difference vegetation index (NDVI) associated with this phenomenon. Multiple sources of PWV values derived from the Global Positioning System (GPS), Radiosonde (RS) and European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim data are used with different spatiotemporal resolutions. The monthly averaged PWV in August 2014 exceeded the 95% percentiles of climatological value (53 mm) while the monthly averaged temperature was less than the 5% percentiles of climatological value (26.6 °C). Before the extreme precipitation, the PWV increased from the yearly averaged value of about 35 mm to more than 60 mm and gradually returned to the August climatological average of 50 mm after the precipitation ended. A large-scale atmospheric water vapor was partially conveyed by the warm wet air current of anticyclones which originated over the South China Sea (25° N, 130° E) and the Western Pacific Ocean. The monthly NDVI variation over the past 34 years (1982–2015) was investigated in this paper and the significant impact of extreme climate on vegetation growth in August 2014 was found. The extreme negative temperature anomaly and positive PWV anomaly are the major climate-driven factors affecting vegetation growth in the north and south of Zhejiang province with correlation coefficients of 0.83 and 0.72, respectively, while the extreme precipitation does not show any apparent impact on NDVI.

This study presents the development and a systematic evaluation study of GNSS reflectometry wind speeds. After establishing a wind speed retrieval algorithm, UK TechDemoSat-1 (TDS-1) derived winds, from May 2015 to July 2017, are compared to the Advanced Scatterometer (ASCAT). ERA-Interim wind fields of the European Centre for Medium-range Weather Forecasts (ECMWF) andin situobservation from Tropical Atmosphere Ocean buoy array in the Pacific are taken as reference. One-year averaged TDS-1 global winds demonstrate small differences with ECMWF in a majority of areas as well as discuss under- and overestimations. The pioneering TDS-1 winds demonstrate a root-mean-squared error (RMSE) and bias of 2.77 and $-$ 0.33 m/s, which are comparable to the RMSE and bias derived by ASCAT winds, as large as 2.31 and 0.25 m/s, respectively. Using buoys measurements as reference, RMSE and bias of 2.23 and $-$ 0.03 m/s for TDS-1 as well as 1.40 and $-$ 0.68 m/s for ASCAT are obtained. Utilizing rain microwave–infrared estimates of the Tropical Rainfall Measuring Mission, rain-affected observation of both ASCAT and TDS-1 are collected and evaluated. Although ASCAT winds show a significant performance degradation resulting in an RMSE and bias of 3.16 and 1.03 m/s, respectively, during rain condition, TDS-1 shows a more reliable performance with an RMSE and bias of 2.94 and $-$ 0.21 m/s, respectively, which indicates the promising capability of GNSS forward scattering for wind retrievals during rain. A decrease in TDS-1-derived bistatic radar cross sections during rain events, at weak winds, is also demonstrated. [ABSTRACT FROM AUTHOR]

This study contributes to the development of an algorithm to retrieve the Earth’s surface downward longwave radiation (DLR) for 2nd Geostationary Earth Orbit KOrea Multi-Purpose SATellite (GEO-KOMPSAT-2A; GK-2A)/Advanced Meteorological Imager (AMI). Regarding simulation data for algorithm development, we referred to Clouds and the Earth’s Radiant Energy System (CERES), and the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-interim reanalysis data. The clear sky DLR calculations were in good agreement with the Gangneung-Wonju National University (GWNU) Line-By-Line (LBL) model. Compared with CERES data, the Root Mean Square Error (RMSE) was 10.14Wm−2. In the case of cloudy sky DLR, we estimated the cloud base temperature empirically by utilizing cloud liquid water content (LWC) according to the cloud type. As a result, the correlation coefficients with CERES all sky DLRs were greater than 0.99. However, the RMSE between calculated DLR and CERES data was about 16.67Wm−2, due to ice clouds and problems of mismatched spatial and temporal resolutions for input data. This error may be reduced when GK-2A is launched and its products can be used as input data. Accordingly, further study is needed to improve the accuracy of DLR calculation by using high-resolution input data. In addition, when compared with BSRN surface-based observational data and retrieved DLR for all sky, the correlation coefficient was 0.86 and the RMSE was 31.55 Wm−2, which indicates relatively high accuracy. It is expected that increasing the number of experimental Cases will reduce the error. [ABSTRACT FROM AUTHOR]

Global Navigation Satellite System (GNSS) troposphere tomography has become one of the most cost-effective means to obtain three-dimensional (3-d) image of the tropospheric water vapour field. Traditional methods divide the tomography area into a number of 3-d voxels and assume that the water vapour density at any voxel is a constant during the given period. However, such behaviour breaks the spatial continuity of water vapour density in a horizontal direction and the number of unknown parameters needing to be estimated is very large. This is the focus of the paper, which tries to reconstruct the water vapor field using the tomographic technique without imposing empirical horizontal and vertical constraints. The proposed approach introduces the layered functional model in each layer vertically and only an a priori constraint is imposed for the water vapor information at the location of the radiosonde station. The elevation angle mask of 30° is determined according to the distribution of intersections between the satellite rays and different layers, which avoids the impact of ray bending and the error in slant water vapor (SWV) at low elevation angles on the tomographic result. Additionally, an optimal weighting strategy is applied to the established tomographic model to obtain a reasonable result. The tomographic experiment is performed using Global Positioning System (GPS) data of 12 receivers derived from the Satellite Positioning Reference Station Network (SatRef) in Hong Kong. The quality of the established tomographic model is validated under different weather conditions and compared with the conventional tomography method using 31-day data, respectively. The numerical result shows that the proposed method is applicable and superior to the traditional one. Comparisons of integrated water vapour (IWV) of the proposed method with that derived from radiosonde and European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim data show that the root mean square (RMS)/Bias of their differences are 3.2/−0.8 mm and 3.3/−1.7 mm, respectively, while the values of traditional method are 5.1/−3.9 mm and 6.3/−5.9 mm, respectively. Furthermore, the water vapour density profiles are also compared with radiosonde and ECMWF data, and the values of RMS/Bias error for the proposed method are 0.88/0.06 g/m3 and 0.92/−0.08 g/m3, respectively, while the values of the traditional method are 1.33/0.38 g/m3 and 1.59/0.40 g/m3, respectively.

GPS-based water vapor tomography has been proved to be a cost-effective means of obtaining spatial and temporal distribution of atmospheric water vapor. In previous studies, the tomography height is empirically selected without considering the actual characteristics of the local water vapor distribution, and most existing studies only consider the signals passing from the top boundary of the tomography area. Therefore, the observed signals coming out from the side face of the tomography area are excluded as ineffective information, which not only reduces the utilization rate of signals used but also decreases the number of voxels crossed by rays. This becomes the research point of this paper, which studies the possibility of selecting a reasonable tomography boundary and using signals passing from the side face of the tomography area. This paper first tries to determine the tomography height based on the local atmospheric physical property using many years of radiosonde data, and 8 km is selected as the tomography boundary in Hong Kong. The second part focuses on superimposing the signals penetrating from the side face of the tomography area to tomography modeling by introducing a scale factor that is able to determine the water vapor content of each signal with the part that belongs in the tomography area. Finally, a tomography experiment is carried out based on data provided by the Satellite Positioning Reference Station Network (SatRef) in Hong Kong to validate the proposed method. Experimental result demonstrates that the utilization rate of the signal used and the number of voxels crossed by rays are both increased by 30.32% and 12.62%, respectively. The comparison of tomographic integrated water vapor (IWV) derived from different schemes with that from radiosonde and ECMWF data shows that the RMS error of the proposed method (4.1 and 5.1 mm) is smaller than that of the previous method (5.1 and 5.6 mm). In addition, the tomographic water vapor densities derived from different schemes is also compared with those of by radiosonde and ECMWF; the statistical result over the experimental period shows that the proposed method has an average RMS error of 1.23 and 2.12 g/m3, respectively, which is superior to the previous method at 1.60 and 2.43 g/m3, respectively. [ABSTRACT FROM PUBLISHER]

A methodology to retrieve soil moisture (SM) from Soil Moisture and Ocean Salinity (SMOS) data is presented. The method uses a neural network (NN) to find the statistical relationship linking the input data to a reference SM data set. The input data are composed of passive microwaves (L-band SMOS brightness temperatures, Tb 's) complemented with active microwaves (C-band Advanced Scatterometer (ASCAT) backscattering coefficients), and Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) . The reference SM data used to train the NN are the European Centre For Medium-Range Weather Forecasts model predictions. The best configuration of SMOS data to retrieve SM using an NN is using Tb 's measured with both H and V polarizations for incidence angles from 25° to 60°. The inversion of SM can be improved by ∼10% by adding MODIS NDVI and ASCAT backscattering data and by an additional ∼5% by using local information on the maximum and minimum records of SMOS Tb's (or ASCAT backscattering coefficients) and the associated SM values. The NN-inverted SM is able to capture the temporal and spatial variability of the SM reference data set. The temporal variability is better captured when either adding active microwaves or using a local normalization of SMOS Tb's. The NN SM products have been evaluated against in situ measurements, giving results of comparable or better (for some NN configurations) quality to other SM products. The NN used in this paper allows to retrieve SM globally on a daily basis. These results open interesting perspectives such as a near-real-time processor and data assimilation in weather prediction models. [ABSTRACT FROM PUBLISHER]

The total glacial area of the Cordillera Blanca, Peru, has shrunk by more than 30% in the period of 1930 to the present with a marked glacier retreat also in the recent decades. The aim of this paper is to assess local air temperature and precipitation changes in the Cordillera Blanca and to discuss how these variables could have affected the observed glacier retreat between the 1980s and present. A unique data set from a large number of stations in the region of the Cordillera Blanca shows that after a strong air temperature rise of about 0.31°C per decade between 1969 and 1998, a slowdown in the warming to about 0.13°C per decade occurred for the 30years from 1983 to 2012. Additionally, based on data from a long-term meteorological station, it was found that the freezing line altitude during precipitation days has probably not increased significantly in the last 30years. We documented a cooling trend for maximum daily air temperatures and an increase in precipitation of about 60mm/decade since the early 1980s. The strong increase in precipitation in the last 30years probably did not balance the increase of temperature before the 1980s. It is suggested that recent changes in temperature and precipitation alone may not explain the glacial recession within the thirty years from the early 1980s to 2012. Glaciers in the Cordillera Blanca may be still reacting to the positive air temperature rise before 1980. Especially small and low-lying glaciers are characterised by a serious imbalance and may disappear in the near future. [ABSTRACT FROM AUTHOR]

The lack of dense, high quality, long-term in-situ wind data sets and wind resource maps for the Caribbean region is a major impediment to the development of wind energy projects. Thus, there is limited understanding of the large-scale near-surface wind climate and the regional wind resources. Through statistical analyses on 10m level NCEP/DOE reanalysis wind data for the period 1979–2010, this work found that although the prevailing winds are from the east-north-east over the eastern Caribbean islands, their wind direction distributions are bimodal. The regional area-averaged wind speed attains a maximum in January and a secondary maximum in July which coincides with the Caribbean Mid-Summer Drought. The derived regional annual wind resource map shows that the Caribbean low-level jet (CLLJ) region is an area of superb wind power density (WPD), 400–600W/m2, the eastern Caribbean and the Netherland Antilles are locations of excellent resource, 300–400W/m2, and the Greater Antilles and the Bahamas are areas of good-very good resource, 200–300W/m2. In general, WPDs are greater in the dry season than the wet season. The regional mean annual area-averaged WPD is 308W/m2 with mean WPDs of 350W/m2 for the dry season, 290W/m2 for the early rainy season, and 247W/m2 for the late rainy season. Annual WPDs vary within±18% of their mean. The area-averaged WPD ranges from 124 to 592W/m2 (±92% of mean annual WPD) depending on the year, season, or month. Therefore, the reanalysis data are shown to be suitable for general assessments of the wind resources in the Caribbean and thus, may be used as initial and boundary conditions in numerical models for the development of high resolution wind maps through dynamical downscaling. [ABSTRACT FROM AUTHOR]

The launch of the Soil Moisture and Ocean Salinity (SMOS) satellite of the European Space Agency opens the way to using a new type of satellite data that are very sensitive to soil moisture for numerical weather prediction. The European Centre for Medium-Range Weather Forecasts (ECMWF) has developed an operational chain which makes it possible to process SMOS data in near real time (NRT) and compare it with a model equivalent. This process has been very challenging. The main reasons are the particular characteristics of the SMOS observation system and the large volume of data. Despite these obstacles, SMOS data are being processed successfully in NRT within the ECMWF Integrated Forecasting System (IFS). The ultimate objective is to assimilate these data in the IFS. It is expected to have an impact on the weather forecast at short and medium ranges. Prior to assimilation experiments, the quality of the data has to be assessed. This can be done through monitoring activities. Monitoring is a routine task performed with all satellite data, and among other things, it makes it possible to localize temporal (or spatial) bias or drifts in the data, thus providing NRT reports to the calibration and validation teams, which can act accordingly. In this letter, the implementation of SMOS data in the ECMWF IFS for monitoring purposes is discussed. The system was developed using a simulated file for the NRT processor, and it was tested using real data from the first year since the launch date. [ABSTRACT FROM PUBLISHER]

Radar backscatter (σ°) from ergs is modulated with view direction [incidence (θ) and azimuth (ɸ) angles], where the modulation characteristics reflect the surface geometry. σ° also varies spatially and reflects the spatial inhomogeneity of the sand surface. We use σ° measurements at different 6 and 4 angles from the NASA, European Remote Sensing satellite, and SeaWinds scatterometers to understand the relationship between wind and erg bedforms. A model incorporating the σ° ɸ-modulation and spatial inhomogeneity is proposed. Surface slope variations are related to the σ° spatial inhomogeneity. We compare the backscatter model results with numerically predicted wind direction data provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) over the erg surfaces. We use the maxima of the ɸ-modulation at θ = 33° to infer the orientation of the dominant slip-sides on the sand surface. These orientations are consistent with the ECMWF wind directions spatially and temporally. [ABSTRACT FROM AUTHOR]

With the confirmed evidence of rain surface perturbation in recent studies, the rain effects on C-band scatterometer measurements are reevaluated. By using colocated Tropical Rainfall Measuring Mission Precipitation Radar, ESCAT on European Remote Sensing Satellites, and European Centre for Medium-Range Weather Forecasts data, we evaluate the sensitivity of C-band σ° to rain. We develop a low-order wind/rain backscatter model with inputs of surface rain rate, incidence angle, wind speed, wind direction, and azimuth angle. We demonstrate that the wind/rain backscatter model is accurate enough for describing the total backscatter in raining areas with relatively low variance. We also show that the rain surface perturbation is a dominating factor of the rain-induced backscatter. Using three distinct regimes, we show under what conditions the wind, rain, and both wind and rain can be retrieved from the measurements. We find that the effect of rain has a more significant impact on the measurements at high incidence angles than at low incidence angles. [ABSTRACT FROM AUTHOR]

Zhejiang province in China experienced an extreme climate phenomenon in August 2014 with temperature rises, sunshine duration decreases, and precipitation increases, particularly, the successive heavy rainfall events occurring from 16 to 20 August 2014 that contributed to this climate anomaly. This study investigates the spatial-temporal variation characteristics of precipitable water vapor (PWV) and the normalized difference vegetation index (NDVI) associated with this phenomenon. Multiple sources of PWV values derived from the Global Positioning System (GPS), Radiosonde (RS) and European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim data are used with different spatiotemporal resolutions. The monthly averaged PWV in August 2014 exceeded the 95% percentiles of climatological value (53 mm) while the monthly averaged temperature was less than the 5% percentiles of climatological value (26.6 °C). Before the extreme precipitation, the PWV increased from the yearly averaged value of about 35 mm to more than 60 mm and gradually returned to the August climatological average of 50 mm after the precipitation ended. A large-scale atmospheric water vapor was partially conveyed by the warm wet air current of anticyclones which originated over the South China Sea (25° N, 130° E) and the Western Pacific Ocean. The monthly NDVI variation over the past 34 years (1982–2015) was investigated in this paper and the significant impact of extreme climate on vegetation growth in August 2014 was found. The extreme negative temperature anomaly and positive PWV anomaly are the major climate-driven factors affecting vegetation growth in the north and south of Zhejiang province with correlation coefficients of 0.83 and 0.72, respectively, while the extreme precipitation does not show any apparent impact on NDVI. [ABSTRACT FROM AUTHOR]

Subsequent to the launch of an ocean scatterometer onboard the Oceansat-2 satellite, hereby referred to as OSCAT, on September 23, 2009, the nine-month period from November 2009 to July 2010 was the validation phase for the retrieved ocean surface winds. This letter focuses on one section of the validation campaign, where the validation study for OSCAT winds with European Centre for Medium-Range Weather Forecasts (ECMWF) and National Centers for Environmental Prediction (NCEP) analyses is carried out. It is found that, in the 4–24-m/s range of wind speed, the rms error in OSCAT wind speed with ECMWF analysis is 1.4 m/s while that with NCEP analysis is 1.7 m/s. In the case of retrieved wind direction, the rms error is 17.2^\circ with ECMWF analyses, while that for NCEP is 18.8^\circ. These statistics are well within the mission goal of 2-m/s accuracy in wind speed and 20 ^\circ in wind directions. This letter discusses the comparison results for different geographical regions and wind speed ranges. [ABSTRACT FROM PUBLISHER]

Global Navigation Satellite System (GNSS) troposphere tomography has become one of the most cost-effective means to obtain three-dimensional (3-d) image of the tropospheric water vapour field. Traditional methods divide the tomography area into a number of 3-d voxels and assume that the water vapour density at any voxel is a constant during the given period. However, such behaviour breaks the spatial continuity of water vapour density in a horizontal direction and the number of unknown parameters needing to be estimated is very large. This is the focus of the paper, which tries to reconstruct the water vapor field using the tomographic technique without imposing empirical horizontal and vertical constraints. The proposed approach introduces the layered functional model in each layer vertically and only an a priori constraint is imposed for the water vapor information at the location of the radiosonde station. The elevation angle mask of 30° is determined according to the distribution of intersections between the satellite rays and different layers, which avoids the impact of ray bending and the error in slant water vapor (SWV) at low elevation angles on the tomographic result. Additionally, an optimal weighting strategy is applied to the established tomographic model to obtain a reasonable result. The tomographic experiment is performed using Global Positioning System (GPS) data of 12 receivers derived from the Satellite Positioning Reference Station Network (SatRef) in Hong Kong. The quality of the established tomographic model is validated under different weather conditions and compared with the conventional tomography method using 31-day data, respectively. The numerical result shows that the proposed method is applicable and superior to the traditional one. Comparisons of integrated water vapour (IWV) of the proposed method with that derived from radiosonde and European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim data show that the root mean square (RMS)/Bias of their differences are 3.2/−0.8 mm and 3.3/−1.7 mm, respectively, while the values of traditional method are 5.1/−3.9 mm and 6.3/−5.9 mm, respectively. Furthermore, the water vapour density profiles are also compared with radiosonde and ECMWF data, and the values of RMS/Bias error for the proposed method are 0.88/0.06 g/m3 and 0.92/−0.08 g/m3, respectively, while the values of the traditional method are 1.33/0.38 g/m3 and 1.59/0.40 g/m3, respectively. [ABSTRACT FROM AUTHOR]