Search

X-band weather radar can provide high spatial and temporal resolution data, which is essential to precipitation observation and prediction of mesoscale and microscale weather. However, X-band weather radar is susceptible to precipitation attenuation. This paper presents an X-band attenuation correction method based on the light gradient machine (LightGBM) algorithm (the XACL method), then compares it with the ZH correction method and the ZH-KDP comprehensive correction method. The XACL method was validated using observations from two radars in July 2021, the X-band dual-polarization weather radar at the Shouxian National Climatology Observatory of China (SNCOC), and the S-band dual-polarization weather radar at Hefei. During the rainfall cases on July 2021, the results of the attenuation correction were used for precipitation estimation and verified with the rainfall data from 1204 automatic ground-based meteorological network stations in Anhui Province, China. We found that the XACL method produced a significant correction effect and reduced the anomalous correction phenomenon of the comparison methods. The results show that the average error in precipitation estimation by the XACL method was reduced by 39.88% over 1204 meteorological stations, which is better than the effect of the other two correction methods. Thus, the XACL method proved good local adaptability and provided a new X-band attenuation correction scheme. [ABSTRACT FROM AUTHOR]

As clean, renewable energy, photovoltaic (PV) energy can reduce the ozone-layer loss and climate deterioration caused by the use of traditional types of energy to generate electricity. At present, most PV energy products involve the influence of cloud cover on solar radiation. However, the resolution and precision of most cloud cover data are not fine enough to reflect the actual cloud distribution in local areas. This leads to incorrect distribution results of PV energy in areas with high-spatial-variability clouds. Using high-resolution and high-precision cloud cover data obtained by satellite remote sensing to estimate the distribution of PV energy can solve this problem. In this study, the Global Land High-Resolution Cloud Climatology (GLHCC), a 10-day cloud frequency product with a resolution of 1 km and located in China, was used to construct a cloud-based solar radiation estimation model. Using the inverse relationship between cloud cover and solar radiation, the GLHCC was converted into sunshine percentage data. Using meteorological station data in China, a Least Squares Fit (LSF) and error check were carried out on the A-P, Lqbal, Bahel and Sen Models to determine the optimal solar radiation estimation model (Sen Model). Based on the sunshine percentage data, the Sen Model and terrain shielding factors, the distribution of PV energy in China was estimated. Finally, comparing to the Global Horizontal Irradiance (GHI) of the World Bank and the yearly average global irradiance of the Photovoltaic Geographic Information System (PVGIS), PV energy data in this paper more accurately reflected the distribution of PV energy in China, especially in areas with high-spatial-variability clouds. [ABSTRACT FROM AUTHOR]

Severe convective windstorms and tornadoes regularly hit the territory of Russia causing substantial damage and fatalities. An analysis of the climatology and formation environments of these events is essential for risk assessments, forecast improvements and identifying of links with the observed climate change. In this paper, we present an analysis of severe convective windstorms, i.e., squalls and tornadoes reported between 1984 and 2020 in the Perm region (northeast of European Russia), where a local maximum in the frequency of such events was previously found. The analysed database consists of 165 events and includes 100 squalls (convective windstorms), 59 tornadoes, and six cases with both tornadoes and squalls. We used various information to compile the database including weather station reports, damage surveys, media reports, previously presented databases, and satellite images for windthrow. We found that the satellite images of damaged forests are the main data source on tornadoes, but their role is substantially lower for windstorm events due to the larger spatial and temporal scale of such events. Synoptic-scale environments and associated values of convective indices were determined for each event with a known date and time. Similarities and differences for the formation conditions of tornadoes and windstorms were revealed. Both squalls and tornadoes occur mostly on rapidly moving cold fronts or on waving quasi-stationary fronts, associated with low-pressure systems. Analyses of 72-h air parcel backward trajectories shows that the Caspian and Aral Seas are important sources of near-surface moisture for the formation of both squalls and tornadoes. Most of these events are formed within high CAPE and high shear environments, but tornadic storms are generally characterised by a higher wind shear and helicity. We also differentiated convective storms that caused forest damage and those did not. We found the composite parameter WMAXSHEAR is the best discriminator between these two groups. In general, storm events causing windthrow mainly occur under conditions more favourable for deep well-organised convection. Thus, forest damage can be considered as an indicator of the storm severity in the Perm region and in adjacent regions with forest-covered area exceeding 50%. [ABSTRACT FROM AUTHOR]

Clouds play an important role in the energy and moisture cycle of the earth–atmosphere system, which affects many important processes in nature and human societies. However, there are very few fine-grained and high-precision global cloud climatology data available for high-resolution models. In this paper, we produced a fine-grained (1 km resolution) global land cloud climatology (GLHCC) report based on MOD09 cloud masks from 2001 to 2016, with a temporal resolution of 10 days. The two improvements (short-wave infrared and Band 2/6 ratio threshold method) on the original MOD09 cloud mask have reduced the snow, ice, and bright areas mistakenly classified as clouds. The preliminary cloud products undergo the removal of orbital artifacts by Variational Stationary Noise Remover (VSNR) and the removal of abnormal albedo areas to generate the final cloud climatology data. The new product was directly validated by ground-based cloud observations collected from 3777 global weather stations. PATMOS-X from the Advanced Very High Resolution Radiometer (AVHRR) and MOD/MYD35 served as comparison products for consistency check of GLHCC. The assessment results show that GLHCC demonstrated a strong correlation with ground station observations, MOD/MYD35, and PATMOS-X. When the ground observations were taken as the truth value, GLHCC and MOD/MYD35 displayed higher accuracy than PATMOS-X. In most selected interested areas where the three behave differently, GLHCC matched the facts better than MOD/MYD35 and PATMOS-X. The GLHCC can well represent the cloud distribution over the past 16 years and will play an important role in the fine-grained demands of many aspects of nature and human society. [ABSTRACT FROM AUTHOR]

This paper investigates the temperature and precipitation extremes over the Arabian Peninsula using data from the regional climate model RegCM4 forced by three Coupled Model Intercomparison Project Phase 5 (CMIP5) models and ERA–Interim reanalysis data. Indices of extremes are calculated using daily temperature and precipitation data at 27 meteorological stations located across Saudi Arabia in line with the suggested procedure from the Expert Team on Climate Change Detection and Indices (ETCCDI) for the present climate (1986–2005) using 1981–2000 as the reference period. The results show that RegCM4 accurately captures the main features of temperature extremes found in surface observations. The results also show that RegCM4 with the CLM land–surface scheme performs better in the simulation of precipitation and minimum temperature, while the BATS scheme is better than CLM in simulating maximum temperature. Among the three CMIP5 models, the two best performing models are found to accurately reproduce the observations in calculating the extreme indices, while the other is not so successful. The reason for the good performance by these two models is that they successfully capture the circulation patterns and the humidity fields, which in turn influence the temperature and precipitation patterns that determine the extremes over the study region. [ABSTRACT FROM AUTHOR]

Examining the effects of climate change on spring maize, and its suitability under dynamic cultivation patterns, will aid strategic decision-making for future agricultural adaptation. This paper investigates the climate suitability of spring maize, based on daily data from 50 meteorological stations, and statistics on maize yield and area at the county level in Jilin Province, China, between 1986 and 2015. Based on a significant correlation between the cultivation patterns indicator ≥10 °C accumulated temperature (AAT10) and the average yield (R2 = 0.503), the yield data are used to determine suitable thresholds for meteorological factors under the dynamic cultivation pattern, and a fuzzy fitness approach is used to evaluate the climate suitability. The results showed a good agreement between suitability estimates and scaled observed yields (average d = 0.705). Moreover, good consistency between cultivation patterns, climate suitability and yield show that the late-maturing varieties of maize have gradually moved northward and eastward, and the areas of high suitability and high yield have gradually expanded eastward. In addition, drought and chilling hazard factors limit the suitability of climate resources, especially in the eastern and western regions. [ABSTRACT FROM AUTHOR]

Drought is a natural hazard with critical societal and economic consequences to millions of people around the world. In this paper, we present the climatology of severe drought events that occurred during the 20th century in the region of Sao Paulo, Brazil. To account for the effects of rainfall deficit and changes in temperature at a climatic timescale, we chose the Standardized Precipitation Evapotranspiration Index (SPEI) to identify severe droughts over the city of Sao Paulo, and the eastern and central-western regions of the state. Events were identified using weather station data and European Centre for Medium-Range Weather Forecast (ECMWF) reanalysis data, in order to assess the representation of drought periods in both datasets. Results show that the reanalysis seems suitable to represent the number of events and their mean duration, severity and intensity, but the timing and characteristics of individual events are not well reproduced. The correlation between observation and reanalysis SPEI time series is low to moderate in all cases. A linear trend analysis between 1901 and 2010 shows a tendency of increasing (decreasing) severe drought events in the central and western (eastern) Sao Paulo state, according to observational data. This is in agreement with previous findings, and the reanalysis presents this same signal. The weakened trend values in the reanalysis may be associated with issues in representing precipitation in this dataset. [ABSTRACT FROM AUTHOR]

Tree-rings are recorders of environmental signals and are therefore often used to reconstruct past environmental conditions. In this paper, we present four annually resolved, multi-centennial tree-ring isotope series from the southeastern Tibetan plateau. The investigation site, where juniper and spruce trees jointly occur, is one of the highest known tree-stands in the world. Tree ring cellulose oxygen (δ18O) and carbon (δ13C) isotopes were analyzed for a common period of 1685–2007 AD to investigate climate–isotope relationships. Therefore, various climate parameters from a local meteorological station and from the CRU 4.02 dataset were used. Tree-ring δ18O of both species revealed highly significant sensitivities with a high degree of coherence to hydroclimate variables during the growing season. The obtained δ18O–climate relationships can even be retained using a species mean. In contrast, the individual δ13C series indicated a weaker and non-uniform response to the tested variables. Underlying species-specific responses and adaptations to the long-term trend in atmospheric CO2 bias even after a trend correction identified dominant environmental factors triggering the tree-ring δ13C at our site. However, analysis of individual intrinsic water-use efficiency in juniper and spruce trees indicated a species-specific adaptation strategy to climate change. [ABSTRACT FROM AUTHOR]