ECMWF Reanalysis Data Research Articles

STFM: Accurate Spatio-Temporal Fusion Model for Weather Forecasting

Meteorological prediction is crucial for various sectors, including agriculture, navigation, daily life, disaster prevention, and scientific research. However, traditional numerical weather prediction (NWP) models are constrained by their high computational resource requirements, while the accuracy of deep learning models remains suboptimal. In response to these challenges, we propose a novel deep learning-based model, the Spatiotemporal Fusion Model (STFM), designed to enhance the accuracy of meteorological predictions. Our model leverages Fifth-Generation ECMWF Reanalysis (ERA5) data and introduces two key components: a spatiotemporal encoder module and a spatiotemporal fusion module. The spatiotemporal encoder integrates the strengths of convolutional neural networks (CNNs) and recurrent neural networks (RNNs), effectively capturing both spatial and temporal dependencies. Meanwhile, the spatiotemporal fusion module employs a dual attention mechanism, decomposing spatial attention into global static attention and channel dynamic attention. This approach ensures comprehensive extraction of spatial features from meteorological data. The combination of these modules significantly improves prediction performance. Experimental results demonstrate that STFM excels in extracting spatiotemporal features from reanalysis data, yielding predictions that closely align with observed values. In comparative studies, STFM outperformed other models, achieving a 7% improvement in ground and high-altitude temperature predictions, a 5% enhancement in the prediction of the u/v components of 10 m wind speed, and an increase in the accuracy of potential height and relative humidity predictions by 3% and 1%, respectively. This enhanced performance highlights STFM’s potential to advance the accuracy and reliability of meteorological forecasting.

ASSESSMENT OF REANALYSIS DATA FOR SOLAR PV OUTPUT FORECASTING IN THE PHILIPPINES: CASE OF PANGASINAN, NEGROS OCCIDENTAL, AND DAVAO DEL NORTE

Abstract. The sustainable energy transition in the Philippines requires accurate forecasting of solar PV output to optimize energy efficiency and grid management. While existing studies have emphasized the positive correlation between solar irradiance and PV production, this study aims to explore whether forecasting improves with the inclusion of weather data. This research conducts a comparative analysis between relying solely on solar irradiance against integrating various weather parameters to enhance solar PV output forecasting. The study focuses on three distinct locations (Pangasinan, Negros Occidental, and Davao Del Norte) and employs two models per each site: Model 1 (M1), which relies only on solar irradiance as predictors, and Model 2 (M2), which incorporates solar irradiance and weather parameters. Using Fifth Generation ECMWF Reanalysis (ERA5) Data, Principal Component Analysis (PCA) is conducted on the significant weather parameters. Extreme Gradient Boosting (XGBoost) with 5-fold nested cross-validation is applied for solar PV output forecasting. Models are assessed using Mean Absolute Percentage Error (MAPE) and skill scores. Results showthat while solar irradiance alone suffices for predicting solar PV output in Negros Occidental, incorporating weather parameters improves forecasting accuracy in Davao Del Norte and Pangasinan. This paper recommends caution in generalizing the findings to different regions with varying weather patterns, as the forecasting performance of the models is influenced by data quality, specific location, and prevailing weather conditions.

Lower Atmospheric Ducts over the South China Sea Related to the Monsoon, Atmospheric, and Ocean Conditions Based on ECMWF Reanalysis Data

Abstract Atmospheric ducting is a highly refractive propagation condition that frequently occurs at sea and significantly impacts radar and communication equipment. This paper analyzes the spatiotemporal distribution of lower atmospheric ducts (LAD) in the South China Sea (SCS) and the variation of their occurrence rate with the monsoon by using reanalysis data from the ECMWF from 1980 to 2022. Additionally, the study discusses the relationship between ducting occurrences and atmospheric and oceanic conditions. The results indicate that wind dynamics in the SCS significantly impact ducting incidents. During the high-incidence period of LAD, humidity-gradient-constructed ducts are the primary mechanism. Before the onset of the monsoon, the mountains in the western part of Luzon Island obstruct the easterly wind, resulting in high temperatures and strong evaporation along the western coast of the mountains. Meanwhile, low temperatures and humidity prevail in the eastern part of the mountains, and they lead to a stratified atmosphere characterized by dry and cold upper layers and warm and humid lower layers in the western part of Luzon Island, which causes a distinct decrease in humidity with height. After the onset of the monsoon, the air from the Indochina Peninsula to the ocean is dry and cold, but the high-altitude area blocks it. This weakens the horizontal mobility of the low-level humid atmosphere over the sea, resulting in atmospheric stratification in the eastern coastal area of the Indochina Peninsula. This stratification leads to dry and cold upper layers and warm and humid lower layers. Significance Statement Atmospheric ducting is a superrefractive propagation condition that frequently occurs at sea and has a significant impact on radar and communication equipment and is related to large-scale or medium- and small-scale atmospheric stratification. The distribution of land and sea around the South China Sea (SCS) and the monsoon are important factors affecting the existence of atmospheric ducts in this region. Many scholars have studied the mechanism of atmospheric ducts in local areas based on observation data (or reanalysis data). The literature on the atmospheric ducts in the SCS mainly focuses on the spatial and temporal statistical distribution of seasons, months, and days, and gives the spatial and temporal distribution characteristics of the region within the statistical time, emphasizing the important influence of the monsoon on the duct, but there is no relevant research on the reasons for the existence of the specific relationship and its temporal and spatial distribution characteristics. The manuscript analyzes the temporal and spatial distribution of lower atmospheric ducts in the SCS and the variation of their occurrence rate with the monsoon, quantifies the contributions of temperature, humidity, and air pressure to the ducting occurrence, meanwhile discussing the ducting occurrence relation with atmospheric and oceanic conditions. In the end, we demonstrate that the development of high-incidence areas for SCS ducts prior to and following the onset of monsoon season is connected to factors such as wind patterns, seawater evaporation, and topography. Furthermore, unstable vertical transport of water vapor in both the atmosphere and oceanic conditions plays a crucial role in facilitating the creation of humidity-type ducts.

Stratospheric influence on the winter North Atlantic storm track in subseasonal reforecasts

Abstract. Extreme stratospheric polar vortex events, such as sudden stratospheric warmings (SSWs) or extremely strong polar vortex events, can have a significant impact on surface weather in winter. SSWs are most often associated with negative North Atlantic Oscillation (NAO) conditions, cold air outbreaks in the Arctic and a southward-shifted midlatitude storm track in the North Atlantic, while strong polar vortex events tend to be followed by a positive phase of the NAO, relatively warm conditions in the extratropics and a poleward-shifted storm track. Such changes in the storm track position and associated extratropical cyclone frequency over the North Atlantic and Europe can increase the risk of extreme windstorm, flooding or heavy snowfall over populated regions. Skillful predictions of the downward impact of stratospheric polar vortex extremes can therefore improve the predictability of extratropical winter storms on subseasonal timescales. However, there exists a strong inter-event variability in these downward impacts on the tropospheric storm track. Using ECMWF reanalysis data and reforecasts from the Subseasonal to Seasonal (S2S) Prediction Project database, we investigate the stratospheric influence on extratropical cyclones, identified with a cyclone detection algorithm. Following SSWs, there is an equatorward shift in cyclone frequency over the North Atlantic and Europe in reforecasts, and the opposite response is observed after strong polar vortex events, consistent with the response in reanalysis. However, although the response of cyclone frequency following SSWs with a canonical surface impact is typically captured well during weeks 1–4, less than 25 % of the reforecasts manage to capture the response following SSWs with a “non-canonical” impact. This suggests a possible overconfidence in the reforecasts with respect to reanalysis in predicting the canonical response after SSWs, although it only occurs in about two-thirds of the events. The cyclone forecasts following strong polar vortex events are generally more successful. Understanding the role of the stratosphere in subseasonal variability and predictability of storm tracks during winter can provide a key for reliable forecasts of midlatitude storms and their surface impacts.

Different Configurations of Cross-Equatorial Flows over the Indian Ocean–Western Maritime Continent and Their Implications for Improving Regional Climate Predictability

Abstract Low-level cross-equatorial flows (CEFs) over the Indian Ocean–western Maritime Continent (IO-wMC) play a crucial role in transporting energy, mass, and water vapor into the Northern Hemisphere during the Asian summer monsoon season (May–September). Utilizing ECMWF reanalysis data (ERA5), we investigate three CEFs over the IO-wMC: the Somali-CEF, Bay of Bengal CEF (BoB-CEF), and South China Sea CEF (SCS-CEF). The statistical independence of the Somali-CEF and BoB-CEF implies distinct formation processes on the interannual time scale. To examine the interannual variability of CEFs, we perform an empirical orthogonal function (EOF) analysis of vertically integrated meridional winds from surface pressure to 850 hPa over the equatorial IO-wMC. The first EOF mode reveals a weakening of the Somali-CEF and strengthening of the BoB-CEF and SCS-CEF, which is associated with the concurrent summer Indian Ocean dipole and quasi-biennial phase transition of El Niño events. The second EOF is related to the Indo-western Pacific Ocean capacitor (IPOC) mode that often emerges in post–El Niño summers. Despite the IPOC’s influence on meridional winds over the tropical southwestern Indian Ocean, three CEFs are uncorrelated with the EOF2. On the other hand, the Somali-CEF and BoB-CEF are significantly weakened in the EOF3. The EOF3 is an ENSO-unrelated internal IPOC mode. We further use a multilinear regression model based on preceding sea surface temperature (SST) over the Indo-Pacific and EOF3 to predict regional climate anomalies and compare the prediction skill with the SST-based models. Our results suggest that adding EOF2- and EOF3-related variability to the prediction model can improve Asian summer monsoon predictability. Significance Statement This study examines the interannual variability of low-level cross-equatorial flows (CEFs) over the Indian Ocean and western Maritime Continent, crucial components of the Asian summer monsoon. We identify three CEFs and investigate their relationships with interannual sea surface temperature modes in the Indo-Pacific region. The leading empirical orthogonal function (EOF) modes of equatorial meridional wind reveal diverse CEF configurations and post–El Niño summer wind variability. Also, the EOFs distinguishes between El Niño and El Niño–unrelated equatorial meridional wind variability. Utilizing a multilinear regression model based on both preceding Indo-Pacific SST modes and configurations of CEFs, we demonstrate the potential to enhance regional climate predictions, contributing to a more comprehensive understanding and forecasting of the Asian summer monsoon and its associated climate impacts.

The Influence of Sea Surface Temperature From ECMWF Reanalysis Data on the Nonuniformity of Evaporation Duct

The Influence of Sea Surface Temperature From ECMWF Reanalysis Data on the Nonuniformity of Evaporation Duct

Impacts of a Cold‐Wave Event on Civil Aviation in China: A November 2022 Case Study

A cold wave is a hazardous event experienced in the middle latitudes during the winter that significantly influences civil aviation. This study utilized the fifth‐generation ECMWF reanalysis (ERA5) data and aerodrome routine meteorological reports (METARs) from 36 airports in China to analyze the impacts of a cold‐wave event from November 26 to 30, 2022. We assessed the effects on wind, visibility, thunderstorms, precipitation, ground‐icing conditions, in‐flight aircraft icing conditions, and clear air turbulence. The results indicated that regions affected by the cold wave shifted from northwest to southeast, with notable impacts on wind, precipitation, and icing conditions. Strong winds, particularly crosswinds, affected airports in coastal regions and narrow valleys, enhancing wind speeds and creating hazardous conditions. Ground‐icing conditions were prevalent, with 23 out of 36 airports experiencing significant icing, notably in Northeast, Central, southern, and eastern China. In‐flight icing conditions were identified in the lower and middle troposphere, primarily affecting Northwest, Northeast, and Central China and the middle and lower reaches of the Yangtze River. At cruising levels, the cold wave introduced light clear air turbulence. This study provides valuable insights for improving civil aviation operations during cold‐wave events, emphasizing the need for preventive measures and early warning systems to mitigate potential risks.

Diagnosis of Warm-Sector Heavy Rainfall with Warm Shear in the Yangtze–Huaihe Coastal Areas from the Perspective of Moist Static Energy

Based on the Climate Precipitation Center Morphing (CMORPH) precipitation data and the fifth-generation ECMWF reanalysis (ERA5) data, moist static energy (MSE) diagnosis for 14 cases of southerly warm-sector heavy rainfall with warm shear (WSWR) along the Yangtze-Huaihe coastal area (YHCA) was conducted. The results indicate that the vertically integrated MSE tendency peaks before the precipitation reaches its maximum. This suggests a rapid MSE accumulation leading up to precipitation onset, with moist enthalpy advection dominantly influencing this increase. The vertical advection of MSE is negative, suggesting that upward motions and rainfall play a crucial role in consuming MSE. Vertical integrated MSE budget analysis for the nine cases of nocturnal rain shows that moist enthalpy advection was the primary contributor, driven mainly by meridional latent energy advection. Scale analysis shows that the combination of meridional disturbance wind and the mean specific humidity field results in pronounced meridional latent energy advection. For the five cases of non-nocturnal rain, the net energy flux was dominant before the onset of precipitation, primarily driven by clear-sky net shortwave radiation (SWCS). The meridional internal energy advection also makes a substantial contribution. The scale analysis indicates that the combined effects of the meridional disturbance wind and the average temperature field lead to significant meridional internal energy advection.

Comparative Analysis of the Precipitation Difference Between Sanya Airport and Haikou Airport Caused by Typhoon 2117

By using ECMWF and FNI reanalysis data, airport self-observation precipitation data, satellite TBB data, and precipitation distribution data of Hainan Meteorological Bureau, the rainfall characteristics of Sanya Phoenix Airport and Haikou Meilan Airport on the day of Typhoon Lionrock on the 17th, autumn 2021 were analyzed. The results found that: (1) The lifting effect of the terrain and the invasion of weak cold air intensified the intensity of the rainstorm in Hainan. The rainstorm center was located on the windward slope of Wuzhishan Mountain. Fenghuang Airport was in the leeward slope environment, while Meilan Airport was in the windward slope. (2) The convergence of southerly air flow and southeast air flow and the abundant water vapor brought by the dynamic action of upper low-level jet stream are favorable conditions for triggering and maintaining heavy rain. (3) There was a negative correlation between heavy precipitation and brightening temperature of black body. The direct influence system of this weather process was mesoscale convective cloud cluster. (4) The rainstorm occurs in the area of dense θse line, which is conducive to the development of inclined vorticity. (5) The positive vertical helicity of 850hpa is a good indicator of precipitation in the next 6 hours.

Comparison of Moisture Sources and Sinks Estimated with Different Versions of FLEXPART and FLEXPART-WRF Models Forced with ECMWF Reanalysis Data

Abstract Moisture transport and changes in the source–sink relationship play a vital role in the atmospheric branch of the hydrological cycle. Lagrangian approaches have emerged as the dominant tool to account for estimations of moisture sources and sinks; those that use the FLEXPART model fed by ERA-Interim reanalysis are most commonly used. With the release of the higher spatial resolution ERA5, it is crucial to compare the representation of moisture sources and sinks using the FLEXPART Lagrangian model with different resolutions in the input data, as well as its version for WRF-ARW input data, the FLEXPART-WRF. In this study, we compare this model for 2014 and moisture sources for the Iberian Peninsula and moisture sinks of North Atlantic and Mediterranean. For comparison criteria, we considered FLEXPARTv9.0 outputs forced by ERA-Interim reanalysis as “control” values. It is concluded that FLEXPARTv10.3 forced with ERA5 data at various horizontal resolutions (0.5° and 1°) represents moisture source and sink zones as represented forced by ERA-Interim (1°). In addition, the version fed with the dynamic downscaling WRF-ARW outputs (∼20 km), previously forced with ERA5, also represents these patterns accurately, allowing this tool to be used in future investigations at higher resolutions and for regional domains. Significance Statement The FLEXPART dispersion model forced with ERA5 reanalysis data at various resolutions represents moisture source and sink zones compared to when it is forced by ERA-Interim. When the Weather Research and Forecasting Model is used to dynamically downscale ERA5, FLEXPART-WRF can also represent moisture sources and sinks, allowing this tool to be used in future investigations requiring higher resolution and regional domains and on regions with a predominance of complex orography due to its ability to represent local moisture transport.

Enhancement of RegCM4.7‐CLM precipitation and temperature by improved bias correction methods over Central Africa

AbstractPrecipitation and temperature projections from Regional Climate Models (RCMs) over Central Africa (CA) are of great importance. However, several studies have already shown that the data from RCMs cannot be directly used for climate impact studies on a local scale because of systematic biases that characterize them. Therefore, RCM simulations must be preprocessed in order to make them more representative of climate at a local scale. The present study focuses on improving temperature and precipitation simulations from the RegCM4.7 RCM over CA. For this purpose, two correction methods are used: The adjusted Linear Scaling and Variance (Va) methods. Corrected and uncorrected precipitation and near‐surface temperature are compared with Climate Hazards group Infrared Precipitation with Stations (CHIRPS) and the fifth generation of ECMWF reanalysis data (ERA5), respectively. Comparison of the performances of both methods was made during various seasons not only over the whole CA region but also over two sub‐domains (Zones 1 and 2) having different characteristics. This was done on the basis of the mean bias and Root Mean Square Error. Results show that the precipitation from RegCM4 is tainted with huge wet biases compared to CHIRPS. Overall, the analysis suggests that the Va method is the most suitable for reducing the biases of RegCM4.7 simulations, particularly for precipitation irrespective of regions or seasons. However, it has been found that both methods fail to improve temperature biases in the Inter‐Tropical Convergence Zone.

Effects of Land Cover Change and Deforestation on Rainfall and Surface Temperature in New Capital City of Indonesia

Land cover change and deforestation have a significant impact on climate change. This study investigates the effect of land cover change and deforestation on surface temperatures and extreme rainfall in the New Capital City of Indonesia (IKN), particularly in Samboja and Sepaku Subdistricts, East Kalimantan Province. Land cover change and deforestation were analyzed from Landsat 5 TM, 7 ETM+, and 8 OLI satellite data during 2001-2020. Land cover is divided into four classes: built-up land, water bodies, vegetation, and agricultural area. Rainfall data were obtained from the Integrated Multi-satellite Retrievals for GPM (IMERG) version 6 satellite, and extreme temperatures were taken from fifth-generation ECMWF reanalysis (ERA5) data. A significant decrease in vegetated land area is observed every year, followed by an increase in residential land and buildings (built-up area) and agricultural land. The highest rate of increase was observed in the area of ​​agricultural land. Such change is correlated with an increase in surface temperature in the IKN. The strongest correlation is shown by increased built-up area and agricultural land. The relationship between rainfall and land cover is weak, and the body of water shows a relatively strong relationship. The extreme number of very wet days (R95p), ​​consecutive dry days (CDD), and max 1-day precipitation (RX1day) rain index showed a decreasing trend during 2001-2020. However, the consecutive wet days (CWDs) index showed an increase. This needs to be a concern because consecutive precipitation extremes may cause more catastrophability than occasional extreme events.

Hyperspectral Infrared Atmospheric Sounder (HIRAS) Atmospheric Sounding System

Accurate atmospheric temperature and moisture profiles are essential for weather forecasts and research. Satellite-based hyperspectral infrared observations are meaningful in detecting atmospheric profiles, especially over oceans where conventional observations can seldom be used. In this study, a HIRAS (Hyperspectral Infrared Atmospheric Sounder) Atmospheric Sounding System (HASS) was introduced, which retrieves atmospheric temperature and moisture profiles using a one-dimension variational scheme based on HIRAS observations. A total of 274 channels were optimally selected from the entire HIRAS spectrum through information entropy analyses, and a group of retrieval experiments were independently performed for different HIRAS fields of views (FOVs). Compared with the ECMWF reanalysis data version-5 (ERA5), the RMSEs of temperature (relative humidity) for low-, mid-, and high-troposphere layers were 1.5 K (22.3%), 1.0 K (33.2%), and 1.3 K (38.5%), respectively, which were similar in magnitude to those derived from other hyperspectral infrared sounders. Meanwhile, the retrieved temperature RMSEs with respect to the satellite radio occultation (RO) products increased to 1.7 K, 1.8 K, and 1.9 K for the low-, mid-, and high-troposphere layers, respectively, which could be attributed to the accurate RO temperature products in the upper atmospheres. It was also found that the RMSE varied with the FOVs and latitude, which may be caused by the current angle-dependent bias correction and unique background profiles.

Development of a hybrid wind field for modeling the tropical cyclone wave field

Due to the increasing trend of tropical cyclone occurrence in the recent decades, the necessity of developing models for forecasting and hindcasting these atmospheric-oceanic hazards, becomes more apparent. One of the uncertainties about the tropical cyclones is the wind field accuracy of modeling oceanic waves and flows. In this paper, six different wind fields have been examined for hindcasting the wave field of cyclone Gonu within the areas of the Arabian Sea and the Gulf of Oman. The wind field of ECMWF (ERA-Interim) and Climate Forecast System Reanalysis (CFSR) data, that obtained via weather research forecasting (WRF) mesoscale modeling and three wind fields achieved via Holland, Rankin, Young and Sobey (S&Y) parametric models have been investigated as well. The wave field modeling results have been compared with the third-generation SWAN model in two stations within the area of Chabahar Bay. The wind field results obtained via the reanalysis models and WRF one indicate an appropriate estimation of the wave characteristics and the maximum wave height at the measurement stations. In order to better evaluate the results of different wind fields, the maximum wave height maps have been studied in the simulation time period. The results indicated that the maximum wave height values obtained from the reanalysis data do not match the track and development direction of the cyclone Gonu. The mismatch between storm development track and maximum wave height in WRF data can also be observed. The maximum significant wave height obtained via the parametric wind fields is very compatible with the cyclone Gonu track. Examining the maximum wave heights obtained via different wind fields, along with comparing the wave characteristics at the measurement stations, highlights the effect of different wind fields in hindcasting the wave characteristics, especially on the northern coasts of the Gulf of Oman. Examination of the maximum wave heights obtained from different wind fields has been investigated in this study along with comparison of wave characteristics at measurement stations. Results indicated that the optimal approach for simulating the tropical cyclone's wave field is to use a hybrid one. Therefore, a hybrid wind field has been developed which includes the wind characteristics of the parametric model in the areas near the storm source and the WRF meso scale model in the outer zone. The hybrid wind field has led to an improvement in the characteristics of waves within deep and coastal waters.

Remote sensing of solar surface radiation – a reflection of concepts, applications and input data based on experience with the effective cloud albedo

Abstract. Accurate solar surface irradiance (SSI) data are a prerequisite for efficient planning and operation of solar energy systems. Respective data are also essential for climate monitoring and analysis. Satellite-based SSI has grown in importance over the last few decades. However, a retrieval method is needed to relate the measured radiances at the satellite to the solar surface irradiance. In a widespread classical approach, these radiances are used directly to derive the effective cloud albedo (CAL) as basis for the estimation of the solar surface irradiance. This approach was already introduced and discussed in the early 1980s. Various approaches are briefly discussed and analysed, including an overview of open questions and opportunities for improvement. Special emphasis is placed on the reflection of fundamental physical laws and atmospheric measurement techniques. In addition, atmospheric input data and key applications are briefly discussed. It is concluded that the well-established observation-based CAL approach is still an excellent choice for the retrieval of the cloud transmission. The coupling with lookup-table-based clear-sky models enables the estimation of solar surface irradiance with high accuracy and homogeneity. This could explain why, despite its age, the direct CAL approach is still used by key players in energy meteorology and the climate community. For the clear-sky input data, it is recommended to use ECMWF forecast and reanalysis data.

Joint Occurrence of Heavy PM2.5 Pollution Episodes and Persistent Foggy Days in Central East China

Although many severe pollution events in Central and East China have been analyzed in recent years, the heavy PM2.5 pollution episode happened on persistent foggy days from January 13 to 18, 2018 was unique, characterized by explosive increase and sharp decrease in PM2.5 (particles with kinetic equivalent diameter less than or equal to 2.5 microns) concentration. Based on hourly data of ground level meteorological parameters, PM2.5 data and CALIPSO-based (the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) aerosol data, combined with ECMWF (European Centre for Medium-Range Weather Forecasts) reanalysis data and radiosonde temperature profile, a comprehensive analysis was conducted to reveal the meteorological reasons for the evolution of the episode at horizontal and vertical scales. The PM2.5 concentration experienced four stages: a slow-increase phase, rapid-increase phase, rapid-decrease phase, and rebound phase. Results show that because Central and East China (CEC) were located at the back of a high-pressure system, humid southerly winds and near surface inversion (NSI) were responsible for the slow accumulation of pollutants. The rapid-increase phase was attributed to pollution transport at both ground level and in the lower troposphere because of weak cold air invasion. The significant subsidence at 500 hPa and 700 hPa intensified the NSI and led to dense fog. In that case, corresponding to the supersaturated atmosphere, the particles entered the fog droplets and were scavenged partly by deposition at night and were resuspended on the next day when the atmosphere was unsaturated. Our findings provide convincing evidence that surface PM2.5 rapid-decrease phase and the rebound phase were closely associated with dense fog process.

Intraseasonal variability and possible causes of large-scale and convective precipitations over the Gangetic plain of India

The occurrence of Indian summer monsoon rainfall (ISMR) during June to September, especially over the eastern Gangetic plain of India, is the lifeline for densely residing people. The intraseasonal and interannual variability in ISMR provokes drought and flood conditions and largely affects agriculture practices. Owing to its importance, several studies on the variability of ISMR over the meteorological subdivisions, namely West Bengal (WB), Jharkhand (JH), Bihar (BR), East Uttar Pradesh (EUP), and West Uttar Pradesh (WUP), respectively, have been conducted; however, the contribution of large-scale precipitation (LSP) and convective precipitation (CP) in ISMR needs to discuss. The LSP is precipitated out from the stratus or nimbostratus clouds, while CP occurs from the cumulus and cumulonimbus clouds, and both of them coexist in ISMR during summer monsoon months. The objective of this paper is to analyze and discuss the climatological characteristics and possible causes of occurrence of LSP and CP over the meteorological subdivisions. For this purpose, the data of LSP, CP, zonal, meridional (u and v components) wind, and relative humidity (rh) at the spatial resolution of 0.25° × 0.25° for the period of 1980–2019 are taken from the European Centre for Medium-Range Weather Forecasts (ECMWF). The outgoing longwave radiation (OLR) data at a surface resolution of 1° × 1° for the same periods are obtained from the National Centre for Environmental Information (of NOAA). The observed rainfall data of the India Meteorological Department at the same resolution and period is considered and compared with ECMWF Reanalysis (ERA5) data. The spatial and temporal distribution of both types of precipitation is analyzed, and their linkage with OLR, zonal winds, and rh at pressure levels of 1000 hPa, 850 hPa, and 700 hPa (in lower troposphere) is examined. The daily climatological values of CP (LSP) are relatively higher (lower) in each meteorological subdivision. The associated lower values of OLR are noticed over the Gangetic West Bengal, Jharkhand, and Bihar, while higher values of OLR are seen over the East and West UP. From the pressure levels of 1000 to 700 hPa, the change in the zonal wind, i.e., easterly to westerly and vice versa, and occurrence of a large amount of rh (>80%) may be possibly initiated moist convective activity for more precipitating out CP over the Gangetic West Bengal, Jharkhand, and Bihar in comparison to East and West Uttar Pradesh.

Changing Causes of Drought in the Urmia Lake Basin—Increasing Influence of Evaporation and Disappearing Snow Cover

The water level of the Urmia Lake Basin (ULB), located in the northwest of Iran, started to decline dramatically about two decades ago. As a result, the area has become the focus of increasing scientific research. In order to improve understanding of the connections between declining lake level and changing local drought conditions, three common drought indices are employed to analyze the period 1981–2018: The Standard Precipitation Index (SPI), the Standard Precipitation-Evaporation Index (SPEI), and the Standardized Snow Melt and Rain Index (SMRI). Although rainfall is a significant indicator of water availability, temperature is also a key factor since it determines rates of evapotranspiration and snowmelt. These different processes are captured by the three drought indices mentioned above to describe drought in the catchment. Therefore, the main objective of this paper is to provide a comparative analysis of drought over the ULB by incorporating different drought indices. Since there is not enough long-term observational data of sufficiently high density for the ULB region, ECMWF Reanalysis data version 5(ERA5) has been used to estimate SPI, SPEI, and SMRI drought indicators. These are shown to work well, with AUC-ROC > 0.9, in capturing different classes of basin drought characteristics. The results show a downward trend for SPEI and SMRI (but not for SPI), suggesting that both evaporation and lack of snowmelt exacerbate droughts. Owing to the increasing temperatures in the basin and the decrease in snowfall, drought events have become particularly pronounced in the SPEI and SMRI time series since 1995. No significant SMRI drought was detected prior to 1995, thus indicating that sufficient snowfall was available at the beginning of the study period. The study results also reveal that the decrease in lake water level from 2010 to 2018 was not only caused by changes in the water balance components, but also by unsustainable water management.

Influence of SST in Low Latitudes on the Arctic Warming and Sea Ice

Global climate models, focused on projecting anthropogenic warming, have not detected an increase in sea surface temperature (SST) at low latitudes comparable to the observed one. This appears to be one reason for the discrepancy between the model estimates of warming and reduction of the sea ice extent in the Arctic and the observed changes in the climate system. In previous studies, it was shown that short-term manifestations of the impact of low latitudes on the Arctic climate were identified in 2–3 weeks as a result of strengthening of atmospheric circulation patterns. In this paper, for the first time, a climatic relationship was established among an increase in SST, air temperature, and water vapor content at low latitudes, and a decrease in sea ice extent in the Arctic. ECMWF Re-Analysis data (ERA-Interim, ERA5), Hadley Centre Sea Ice and Sea Surface Temperature data set (HadISST), sea ice archives of the World Centers NSIDC (USA), and Arctic and Antarctic Research Institute (Russia), observations of water temperature in the Kola section (33°30’ E), calculated sea ice parameters using the Arctic and Antarctic Research Institute coupled ice-ocean circulation model (AARI–IOCM). Methods of multivariate correlation analysis, calculating spectra and coherence, and creating correlation graphs were used to obtain the results. For the first time, estimates of the effect of heat transport from low to high latitudes on climate change and sea ice extent in the Arctic over the past 40 years have been obtained, explaining a significant part of their variability. The increase in heat transport is affected by an increase in SST at low latitudes, where a significant part of the solar heat is accumulated. Due to the increase in SST, the amount of heat transported by the ocean and the atmosphere from low latitudes to the Arctic increases, leading to an increase in the air temperature, water vapor content, downward longwave radiation at high latitudes, and a decrease in the thickness and extent of winter sea ice. Potential topics include, but are not limited to: the role of heat and moisture transport in the Arctic warming, effect of SST at low latitudes on transports, linkage of warming in low latitudes and in shrinking of the Arctic sea ice.

Satellite Retrieval of Microwave Land Surface Emissivity under Clear and Cloudy Skies in China Using Observations from AMSR-E and MODIS

Microwave land surface emissivity (MLSE) is an important geophysical parameter to determine the microwave radiative transfer over land and has broad applications in satellite remote sensing of atmospheric parameters (e.g., precipitation, cloud properties), land surface parameters (e.g., soil moisture, vegetation properties), and the parameters of interactions between atmosphere and terrestrial ecosystem (e.g., evapotranspiration rate, gross primary production rate). In this study, MLSE in China under both clear and cloudy sky conditions was retrieved using satellite passive microwave measurements from Aqua Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E), combined with visible/infrared observations from Aqua Moderate Resolution Imaging Spectroradiometer (MODIS), and the European Centre for Medium-Range Weather Forecasts (ECMWF) atmosphere reanalysis dataset of ERA-20C. Attenuations from atmospheric oxygen and water vapor, as well as the emissions and scatterings from cloud particles are taken into account using a microwave radiation transfer model to do atmosphere corrections. All cloud parameters needed are derived from MODIS visible and infrared instantaneous measurements. Ancillary surface skin temperature as well as atmospheric temperature-humidity profiles are collected from ECMWF reanalysis data. Quality control and sensitivity analyses were conducted for the input variables of surface skin temperature, air temperature, and atmospheric humidity. The ground-based validations show acceptable biases of primary input parameters (skin temperature, 2 m air temperature, near surface relative humidity, rain flag) for retrieving using. The subsequent sensitivity tests suggest that 10 K bias of skin temperature or observed brightness temperature may result in a 4% (~0.04) or 7% (0.07) retrieving error in MLSE at 23.5 GHz. A nonlinear sensitivity in the same magnitude is found for air temperature perturbation, while the sensitivity is less than 1% for 300 g/m2 error in cloud water path. Results show that our algorithm can successfully retrieve MLSE over 90% of the satellite detected land surface area in a typical cloudy day (cloud fraction of 64%), which is considerably higher than that of the 29% area by the clear-sky only algorithms. The spatial distribution of MLSE in China is highly dependent on the land surface types and topography. The retrieved MLSE is assessed by compared with other existing clear-sky AMSR-E emissivity products and the vegetation optical depth (VOD) product. Overall, high consistencies are shown for the MLSE retrieved in this study with other AMSR-E emissivity products across China though noticeable discrepancies are observed in Tibetan Plateau and Qinling-Taihang Mountains due to different sources of input skin temperature. In addition, the retrieved MLSE exhibits strong positive correlations in spatial patterns with microwave vegetation optical depth reported in the literature.