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We present a global-scale evaluation of surface shortwave (SW↓) radiative fluxes as derived with cloud amount information from the U.S. Air Force (USAF) Cloud Depiction Forecast System (CDFS) II World-Wide Merged Cloud Analysis (WWMCA) and implemented in the framework of the NASA Land Information System (LIS). Evaluation of this product is done against ground observations, a satellite-based product from the Moderate Resolution Imaging Spectroradiometer (MODIS), and several reanalysis outputs. While the LIS/USAF product tends to overestimate the SW↓ fluxes when compared to ground observations and satellite estimates, its performance is comparable or better than the following reanalysis products: ERA5, CFSR, and MERRA-2. Results are presented using all available observations over the globe and independently for several regional domains of interest. When evaluated against ground observations over the globe, the bias in the LIS/USAF product at daily time scale was about 9.34 W m−2 and the RMS was 29.20 W m−2 while over the United States the bias was about 10.65 W m−2 and the RMS was 35.31 W m−2. The sample sizes used were not uniform over the different regions, and the quality of both ground truth and the outputs of the other products may vary regionally. It is important to note that the LIS/USAF is a near-real-time (NRT) product of interest for potential users and as such fills a need that is not met by most products. Due to latency issues, the level of observational inputs in the NRT product is less than in the reanalysis data. Significance Statement We evaluate a current scheme to produce surface radiative fluxes in the NASA Land Information System (LIS) framework as driven with cloud amount information from the U.S. Air Force (USAF) Cloud Depiction Forecast System (CDFS) II World-Wide Merged Cloud Analysis (WWMCA). The LIS/USAF product is provided at near–real time and as such, fills a need that is not met by most products. Information used for evaluation are ground observations, MODIS satellite-based estimates, and independent outputs from several reanalysis. Since the various LIS products are used by the hydrometeorology community, this manuscript should be of interest to the users of the LIS/USAF information on surface radiative fluxes.

Under the GOES-R activity, new algorithms are being developed at the National Oceanic and Atmospheric Administration (NOAA)/Center for Satellite Applications and Research (STAR) to derive surface and top-of-the-atmosphere (TOA) shortwave (SW) radiative fluxes from the Advanced Baseline Imager (ABI), the primary instrument on GOES-R. This paper describes a support effort in the development and evaluation of the ABI instrument capabilities to derive such fluxes. Specifically, scene-dependent narrow-to-broadband (NTB) transformations are developed to facilitate the use of observations from ABI at the TOA. Simulations of NTB transformations have been performed with MODTRAN 4.3 using an updated selection of atmospheric profiles and implemented with the final ABI specifications. These are combined with angular distribution models (ADMs), which are a synergy of ADMs from the Clouds and the Earth's Radiant Energy System (CERES) and from simulations. Surface conditions at the scale of the ABI products as needed to compute the TOA radiative fluxes come from the International Geosphere–Biosphere Programme (IGBP). Land classifications at 1/6∘ resolution for 18 surface types are converted to the ABI 2 km grid over the contiguous United States (CONUS) and subsequently re-grouped to 12 IGBP types to match the classification of the CERES ADMs. In the simulations, default information on aerosols and clouds is based on that used in MODTRAN. Comparison of derived fluxes at the TOA is made with those from CERES, and the level of agreement for both clear and cloudy conditions is documented. Possible reasons for differences are discussed. The product is archived and can be downloaded from the NOAA Comprehensive Large Array-data Stewardship System (CLASS).

Fine particulate matter with a diameter of less than 2.5 µm ( PM2.5 ) has been used as an important atmospheric environmental parameter mainly because of its impact on human health. PM2.5 is affected by both natural and anthropogenic factors that usually have strong diurnal variations. Such information helps toward understanding the causes of air pollution, as well as our adaptation to it. Most existing PM2.5 products have been derived from polar-orbiting satellites. This study exploits the use of the next-generation geostationary meteorological satellite Himawari-8/AHI (Advanced Himawari Imager) to document the diurnal variation in PM2.5 . Given the huge volume of satellite data, based on the idea of gradient boosting, a highly efficient tree-based Light Gradient Boosting Machine (LightGBM) method by involving the spatiotemporal characteristics of air pollution, namely the space-time LightGBM (STLG) model, is developed. An hourly PM2.5 dataset for China (i.e., ChinaHigh PM2.5 ) at a 5 km spatial resolution is derived based on Himawari-8/AHI aerosol products with additional environmental variables. Hourly PM2.5 estimates (number of data samples = 1 415 188) are well correlated with ground measurements in China (cross-validation coefficient of determination, CV- R2 = 0.85), with a root-mean-square error (RMSE) and mean absolute error (MAE) of 13.62 and 8.49 µg m−3 , respectively. Our model captures well the PM2.5 diurnal variations showing that pollution increases gradually in the morning, reaching a peak at about 10:00 LT (GMT + 8), then decreases steadily until sunset. The proposed approach outperforms most traditional statistical regression and tree-based machine-learning models with a much lower computational burden in terms of speed and memory, making it most suitable for routine pollution monitoring.

Nitrogen dioxide (NO

Land surface temperature (LST) is an important climate parameter that controls the surface energy budget. For climate applications, information is needed at the global scale with representation of the diurnal cycle. To achieve global coverage there is a need to merge about five independent geostationary (GEO) satellites that have different observing capabilities. An issue of practical importance is the merging of independent satellite observations in areas of overlap. An optimal approach in such areas could eliminate the need for redundant computations by differently viewing satellites. We use a previously developed approach to derive information on LST from GOES-East (GOES-E), modify it for application to GOES-West (GOES-W) and implement it simultaneously across areas of overlap at 5-km spatial resolution. We evaluate the GOES-based LST against in situ observations and an independent MODIS product for the period of 2004–09. The methodology proposed minimizes differences between satellites in areas of overlap. The mean and median values of the differences in monthly mean LST retrieved from GOES-E and GOES-W at 0600 UTC for July are 0.01 and 0.11 K, respectively. Similarly, at 1800 UTC the respective mean and median value of the differences were 0.15 and 1.33 K. These findings can provide guidelines for potential users to decide whether the reported accuracy based on one satellite alone, meets their needs in area of overlap. Since the 6 yr record of LST was produced at hourly time scale, the data are well suited to address scientific issues that require the representation of LST diurnal cycle or the diurnal temperature range (DTR).

We investigate the spatial and temporal variability of the net ocean–atmosphere heat flux (Q net) over the Indian Ocean (north of 30 S) for the period 2005–2008, as estimated from remotely sensed o...

Under the GOES-R activity, new algorithms are being developed at the National Oceanic and Atmospheric Administration (NOAA)/Center for Satellite Applications and Research (STAR) to derive surface and Top of the Atmosphere (TOA) shortwave (SW) radiative fluxes from the Advanced Baseline Imager (ABI), the primary instrument on GOES-R. This paper describes a support effort in the development and evaluation of the ABI instrument capabilities to derive such fluxes. Specifically, scene dependent narrow-to-broadband (NTB) transformations are developed to facilitate the use of observations from ABI at the TOA. Simulations of NTB transformations have been performed with MODTRAN4.3 using an updated selection of atmospheric profiles as implemented with the final ABI specifications. These are combined with Angular Distribution Models (ADMs), which are a synergy of ADMs from the Clouds and the Earth's Radiant Energy System (CERES) and from simulations. Surface condition at the scale of the ABI products as needed to compute the TOA radiative fluxes come from the International Geosphere-Biosphere Programme (IGBP). Land classification at 1/6° resolution for 18 surface types are converted to the ABI 2-km grid over the (CONtiguous States of the United States) (CONUS) and subsequently re-grouped to 12 IGBP types to match the classification of the CERES ADMs. In the simulations, default information on aerosols and clouds is based on the ones used in MODTRAN. Comparison of derived fluxes at the TOA is made with those from the CERES and/or the Fast Longwave and Shortwave Radiative Flux (FLASHFlux) data. A satisfactory agreement between the fluxes was observed and possible reasons for differences have been identified; the agreement of the fluxes at the TOA for predominantly clear sky conditions was found to be better than for cloudy sky due to possible time shift in observation times between the two observing systems that might have affected the position of the clouds during such periods.

The strategic location of the AERONET site in Ilorin, Nigeria, makes it possible to obtain information on several aerosol types and their radiative effects. The strong reversal of wind direction occasioned by the movement of the ITCZ during the West Africa Monsoon (WAM) plays a major role in the variability of aerosol nature at this site, which is confirmed by aerosol optical depth (AOD) (675 nm) and Angstrom exponent (AE) (440–870 nm) values with 1st and 99th percentile values of 0.08 and 2.16, and 0.11 and 1.47, respectively. The direct radiative forcing (DRF) and radiative forcing efficiency (RFE) of aerosol, as retrieved from the AERONET sun-photometer measurements, are estimated using radiative transfer calculations for the periods of 2005–2009 and 2011–2015. The DRF and RFE of the dominant aerosol classes—desert dust (DD), biomass burning (BB), urban (UB) and gas flaring (GF)—have been estimated. The median (± standard deviation) values of the DRF at the top of the atmosphere (TOA) for the DD, BB, UB and GF aerosol classes are –27.5 ± 13.2 Wm–2, –27.1 ± 8.3 Wm–2, –11.5 ± 13.2 Wm–2 and –9.6 ± 8.0 Wm–2, respectively, while those of the RFE are –26.2 ± 4.1 Wm–2 δ–1, –35.2 ± 4.6 Wm–2 δ–1, –31.0 ± 8.4 Wm–2 δ–1 and –37.0 ± 10.3 Wm–2 δ–1, respectively. Arguably due to its high SSA and assymetric values, the DD aerosol class shows the largest DRF but the smallest RFE. Its smallest AOD notwithstanding, the GF class can cause greater perturbation of the earth-atmosphere system in the sub-region both directly and indirectly, possibly due to the presence of black carbon and other co-emitted aerosol and the ageing of the GF aerosols. This study presents the first estimate of DRF for aerosols of gas flaring origin and shows that its radiative potential can be similar in magnitude to that of biomass burning and urban aerosol in West Africa.

The significance of the diurnal variability of Lake Surface Temperature (LST) has been recognized, yet, its magnitude in terms of spatial and temporal variability is not well known. Attempts have been made to derive such information from satellites at a high spatial resolution, however, most have been made from polar orbiting satellites that sample only twice per day. We have developed an approach to derive such information from geostationary satellites at an hourly time scale and at a spatial resolution of about 5 km. The approach to derive LST uses the Radiative Transfer for TIROS Operational Vertical Sounder (TOVS) (RTTOV) model driven by the Modern-Era Retrospective analysis for Research and Applications (MERRA)-2 information. The methodology has been implemented over Lake Huron for about six years. We present the results of the evaluation against various independent satellite products and demonstrate that there is a strong diurnal variability in the skin temperature over the lake and that the lowest and highest values, as derived twice per day from polar orbiting satellites, may not represent the magnitude of the Diurnal Temperature Range (DTR).

PM2.5 has been used as an important atmospheric environmental parameter primarily due to its impact on human health. PM2.5 is affected by both natural and anthropogenic factors that usually have strong diurnal variations. Monitoring it does not only help understand the causes of air pollution but also our adaptation to it. Most existing PM2.5 products have been derived from polar-orbiting satellites. This study exploits the usage of the next-generation geostationary meteorological satellite Himawari-8/AHI in revealing its diurnal variations. Given the huge volume of the satellite data, a highly efficient tree-based Light Gradient Boosting Machine (LightGBM) learning approach, which is based on the idea of gradient boosting, is applied by involving the spatiotemporal characteristics of air pollution, named the space-time LightGBM (STLG) model. Hourly PM2.5 data set in China (i.e., ChinaHighPM2.5) at a 5 km spatial resolution is derived based on the Himawari-8/AHI aerosol products together with other variables. The hourly PM2.5 estimates (N = 1,415,188) are well correlated with ground measurements (R2 = 0.85) with a RMSE and MAE of 13.62 and 8.49 μg/m3 respectively in China. Our model can capture well the PM2.5 diurnal variations, where the pollution increases gradually in the morning, and reaches a peak at about 10:00 a.m. local time, then decreases steadily until sunset. The proposed approach outperforms most traditional statistical regression and tree-based machine learning models with a much lower computation burden in terms of speed and memory, making it most suitable for routine pollution monitoring.

Ozone (O3) is an important trace and greenhouse gas in the atmosphere, posing a threat to the ecological environment and human health at the ground level. Large-scale and long-term studies of O3 pollution in China are few due to highly limited direct ground and satellite measurements. This study offers a new perspective to estimate ground-level O3 from solar radiation intensity and surface temperature by employing an extended ensemble learning of the space-time extremely randomized trees (STET) model, together with ground-based observations, remote sensing products, atmospheric reanalysis, and an emission inventory. A full-coverage (100%), high-resolution (10 km) and high-quality daily maximum 8-h average (MDA8) ground-level O3 dataset covering China (called ChinaHighO3) from 2013 to 2020 was generated. Our MDA8 O3 estimates (predictions) are reliable, with an average out-of-sample (out-of-station) coefficient of determination of 0.87 (0.80) and root-mean-square error of 17.10 (21.10) μg/m3 in China. The unique advantage of the full coverage of our dataset allowed us to accurately capture a short-term severe O3 pollution exposure event that took place from 23 April to 8 May in 2020. Also, a rapid increase and recovery of O3 concentrations associated with variations in anthropogenic emissions were seen during and after the COVID-19 lockdown, respectively. Trends in O3 concentration showed an average growth rate of 2.49 μg/m3/yr (p

The Dead Sea is a terminal hypersaline lake with a depth of ~300 m, at a unique location approximately 430 m below sea level. Because of very high salinity of ~300 g/kg of Dead Sea water, the non-linear absorption of solar radiation is of an order of magnitude greater than that in fresh-water lakes. Consequently, by contrast to surface water temperature in fresh-water lakes, Dead Sea surface temperature is influenced by wind speed and water mixing. In the absence of vertical water mixing under weak winds, solar radiation in the summer months leads to significant warming of Dead Sea surface water. Under such conditions, daytime sea surface temperature (SST) could reach land surface temperature (LST) over land areas adjacent to the lake. This could lead to an essential reduction of surface heat flow from land to sea and, consequently, significant surface heating of land areas adjacent to the lake.Pronounced asymmetry has been obtained in daytime surface temperature between the east and west sides of the Dead Sea. This asymmetry was observed in the summer months, under uniform solar radiation. Our findings are based on MODIS data (2002–2016) on board the Terra and Aqua satellites. MODIS data showed that, on average for the 15-year study period, daytime SST over the eastern part of the lake exceeded that over the western part by 5 °C. This SST asymmetry (observed in the absence of surface heat flow from land to sea at the eastern side) was accompanied by the asymmetry in LST over areas adjacent to the Dead Sea. Specifically, LST over areas adjacent to the east side exceeded that over areas adjacent to the west side by 10 °C. Such LST difference is the characteristic feature of the hypersaline Dead Sea. In addition to MODIS records (on board the two orbital satellites - Terra and Aqua), Meteosat Second Generation records (on board the geostationary satellites) proved the presence of daytime SST/LST asymmetry.Regional atmospheric warming led to a decrease in the SST asymmetry during the study period. Temperature difference between daytime SST over the east part and that over the west of the Dead Sea steadily decreased at the rate of 0.32 °C decade-1, based on MODIS/Terra data, and 0.54 °C decade-1, based on MODIS/Aqua data.We found that the Weather Forecast and Research (WRF) model distribution of skin temperature over land and sea does not correspond to satellite observations. At midday, over the sea, WRF was incapable of reproducing the observed SST asymmetry. Over land areas adjacent to both the west and east sides of the lake, WRF incorrectly showed that modeled skin temperature increases with its approach to the coastline. The application to modeling of the observed SST/LST asymmetry in existing regional models will improve simulations of atmospheric dynamics over the Dead Sea. Reference: Kishcha P., Starobinets B., Pinker R., Kunin P., Alpert P. (2020). Spatial non-uniformity of surface temperature of the Dead Sea and adjacent land areas. Remote Sensing, Special Issue: Lake Remote Sensing, 12(1), 107; doi:10.3390/rs12010107.

Two components of SRB, solar radiation reflected to space and solar radiation reaching the surface, are retrieved from the Advanced Baseline Imager (ABI). Physical algorithms are used that combine forward and inverse methods to estimate reflection and transmission and account for all major interactions of the radiation with the atmosphere and the surface. Owing to the improved upstream ABI cloud and aerosol product inputs, and because of availability of calibrated solar reflective bands on ABI, the two products represent an improvement in quality over legacy radiation products. Preliminary evaluation of the two products with reference data indicates that they meet expectations.

Pronounced spatial non-uniformity has been obtained of daytime sea surface temperature (SST) of the Dead Sea and of land surface temperature (LST) over areas adjacent to the Dead Sea. This non-uniformity was observed in the summer months, under uniform solar radiation. Our findings are based on Moderate Resolution Imaging Spectroradiometer (MODIS) data (2002&ndash, 2016) on board the Terra and Aqua satellites. MODIS data showed that, on average for the 15-year study period, daytime SST over the eastern part of the lake (Te) exceeded by 5 &deg, C that over the western part (Tw). This SST non-uniformity (observed in the absence of surface heat flow from land to sea at the eastern side) was accompanied by spatial non-uniform distribution of land surface temperature (LST) over areas adjacent to the Dead Sea. Specifically, LST over areas adjacent to the eastern side exceeded by 10 &deg, C that over areas adjacent to the western side. Our findings of spatial non-uniformity of SST/LST based on MODIS data were supported by Meteosat Second Generation LST records. Regional atmospheric warming led to a decrease in spatial non-uniformity of SST during the study period. Temperature difference between Te and Tw steadily decreased at the rate of 0.32 &deg, C decade&minus, 1, based on MODIS/Terra data, and 0.54 &deg, 1, based on MODIS/Aqua data. Our simulations of monthly skin temperature distribution over the Dead Sea by the Weather Forecast and Research (WRF) model contradict satellite observations. The application to modeling of the observed SST/LST spatial non-uniformity will advance our knowledge of atmospheric dynamics over hypersaline lakes.

The aerosol fine-mode fraction (FMF) at the sub-Sahel AErosol RObotic NETwork (AERONET) site at Ilorin (8°32′N; 4°34′E) is found to be the highest (FMFmean = 0.487) compared to six upper Sahel AERONET sites. The fine-mode aerosol population at the site dominates the coarse mode in core West African Monsoon months of June–July–August (FMFmean = 0.581; Angstrom exponent derivative = 0.44). Correlations (r) of aerosol optical depth (AOD) time series with corresponding seasonal zonal wind (ZW) and meridional wind (MW) speeds of the European Centre for Medium-Range Weather Forecasts at the seven AERONET sites reveal a uniquely strong positive value (r = 0.6) of wet-season AOD and MW at Ilorin. The wet-season FMF distribution at the site is bimodal with a broad mode (peak center = 0.685; half-width = 0.521) attributed to a wide range of industrial/urban aerosols and a narrow mode (peak center = 0.338; half-width = 0.136) attributed to fine dust aerosols, while the dry-season distribution is mono-modal, attributed to a fairly broad dust/biomass burning aerosol mixture (peak center = 0.484; half-width = 0.394). These are corroborated with 7-day back trajectories calculated for core wet- and dry-season months over 2 years indicating mainly high altitude maritime and continental air masses in the wet season and lower altitude Sahara and Sudanian air masses in the dry season. Comparison of inter-annual rainfall and FMF trends indicates coherence of intensifying rainfall in traditional dry-season months (December, January, and February) with decreasing FMF distribution means and increasing FMF distribution widths which are consistent with reducing dust and biomass burning aerosols and growing industrial and urban aerosol sources.

Globally, ambient air pollution claims ~9 million lives yearly, prompting researchers to investigate changes in air quality. Of special interest is the impact of COVID-19 lockdown. Many studies reported substantial improvements in air quality during lockdowns compared with pre-lockdown or as compared with baseline values. Since the lockdown period coincided with the onset of the rainy season in some tropical countries such as Nigeria, it is unclear if such improvements can be fully attributed to the lockdown. We investigate whether significant changes in air quality in Nigeria occurred primarily due to statewide COVID-19 lockdown. We applied a neural network approach to derive monthly average ground-level fine aerosol optical depth (AODf) across Nigeria from year 2001–2020, using the Multi-angle Implementation of Atmospheric Correction (MAIAC) AODs from Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) satellites, AERONET aerosol optical properties, meteorological and spatial parameters. During the year 2020, we found a 21% or 26% decline in average AODf level across Nigeria during lockdown (April) as compared to pre-lockdown (March), or during the easing phase-1 (May) as compared to lockdown, respectively. Throughout the 20-year period, AODf levels were highest in January and lowest in May or June, but not April. Comparison of AODf levels between 2020 and 2019 shows a small decline (1%) in pollution level in April of 2020 compare to 2019. Using a linear time-lag model to compare changes in AODf levels for similar months from 2002 to 2020, we found no significant difference (Levene's test and ANCOVA; α = 0.05) in the pollution levels by year, which indicates that the lockdown did not significantly improve air quality in Nigeria. Impact analysis using multiple linear regression revealed that favorable meteorological conditions due to seasonal change in temperature, relative humidity, planetary boundary layer height, wind speed and rainfall improved air quality during the lockdown., Graphical abstract Unlabelled Image

We investigate the impact of El Nino - Southern Oscillation (ENSO) on surface radiative fluxes over the tropical Pacific using satellite observations and fluxes derived from selected atmospheric re-analyses. Agreement between the two in this region is important because re-analysis information is frequently used to assess surface energy budget sensitivity to ENSO. We found that during the traditional ENSO, the maximum variance of anomalous incoming solar radiation is located just west of the dateline and coincides with the area of largest anomalous SST gradient. It can reach up to ∼60 W/m2 and lags behind the Nino3 index by about a month, suggesting a response to anomalous SST gradient. The magnitude of longwave anomaly is only half that large, and varies in phase with the SST anomaly. Similar anomalies were derived from outputs: from the European Centre for Medium-Weather Forecasts Reanalysis Interim (ERA-I), from the Modern Era Retrospective Analysis version 2 (MERRA-2), from the NCEP/NCAR Re-analysis 1 (R1), and from the Japanese JRA55 re-analysis. Among the four re-analyses used, results from ERA-I are the closest to observations. We have also investigated the surface wind divergence/convergence and found that the main factor limiting eastward excursions of convection is the surface wind convergence. Due to the wind divergence pattern normally present over the eastern cold tongue, anomalous convection extends into the eastern equatorial Pacific only during the strongest warm events. Our analysis also considers the El Nino Modoki events, for which the radiation flux patterns are shifted westward following the SST pattern.

Radiative fluxes are a key component of the surface heat budget of the oceans. Yet, observations over oceanic region are sparse due to the complexity of radiation measurements; moreover, certain oceanic regions are substantially under-sampled, such as the north Indian Ocean. The National Institute of Ocean Technology, Chennai, India, under its Ocean Observation Program has deployed an Ocean Moored Network for the Northern Indian Ocean (OMNI) both in the Arabian Sea and the Bay of Bengal. These buoys are equipped with sensors to measure radiation and rainfall, in addition to other basic meteorological parameters. They are also equipped with sensors to measure sub-surface currents, temperature, and conductivity from the surface up to a depth of 500 m. Observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard the National Aeronautics and Space Administration (NASA) AQUA and TERRA satellites have been used to infer surface radiation over the north Indian Ocean. In this study, we focus only on the shortwave (SW↓) fluxes. The evaluations of the MODIS-based SW↓ fluxes against the RAMA observing network have shown a very good agreement between them, and therefore, we use the MODIS-derived fluxes as a reference for the evaluation of the OMNI observations. In an early deployment of the OMNI buoys, the radiation sensors were placed at 2 m above the sea surface; subsequently, the height of the sensors was raised to 3 m. In this study, we show that there was a substantial improvement in the agreement between the buoy observations and the satellite estimates, once the sensors were raised to higher levels. The correlation coefficient increased from 0.87 to 0.93, and both the bias and standard deviations decreased substantially.

We present results from an evaluation of precipitable water vapor (W) over remote oceanic areas as derived from global reanalysis models and from satellites against observations from the Maritime Aerosol Network (MAN) for cloudless skies during the period of 2004–2017. They cover polar, mid latitude and tropical oceanic regions and represent a first effort to use MAN observations for such evaluation. The global reanalysis model products evaluated in this study are from the Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2), the European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Reanalysis (ERA I), and the Climate Forecast System Reanalysis (CFSR) model. The satellite products evaluated are from the Moderate Resolution Imaging Spectroradiometer (MODIS), the Polarization and Directionality of the Earth's Reflectances (POLDER), the Global Ozone Monitoring Experiment (GOME-2), the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY), and the Atmospheric Infra-red Sounder (AIRS). Satellite retrievals of W are based on the attenuation of solar reflected light by water vapor absorption bands, except those from AIRS that rely on brightness temperature measurements. A very good agreement is observed between the model estimates and MAN, with mean differences of ~5% and standard deviations of ~15%. These results are within the uncertainties associated with the models and the measurements, indicating the skill of the reanalysis models to estimate W over oceans under clear sky conditions. Mean differences of W between the satellite and MAN products are ~11, 6.7, 12, −7, and 3% for MODIS, POLDER, GOME-2, SCIAMACHY and AIRS respectively, while their standard deviations are 31, 29, 28, 20 and 17%. These differences reveal the need to address inconsistencies among different satellite sensors and ground-based measurements to reduce the uncertainties associated with the retrievals.

Snow-covered mountain ranges are a major source of water supply for runoff and groundwater recharge. Snowmelt supplies as much as 75% of the surface water in basins of the western United States. Net radiative fluxes make up about 80% of the energy balance over snow-covered surfaces. Because of the large extent of snow cover and the scarcity of ground observations, use of remotely sensed data is an attractive option for estimating radiative fluxes. Most of the available methods have been applied to low-spatial-resolution satellite observations that do not capture the spatial variability of snow cover, clouds, or aerosols, all of which need to be accounted for to achieve accurate estimates of surface radiative fluxes. The objective of this study is to use high-spatial-resolution observations that are available from the Moderate Resolution Imaging Spectroradiometer (MODIS) to derive surface shortwave (0.2–4.0 μm) downward radiative fluxes in complex terrain, with attention on the effect of topography (e.g., shadowing or limited sky view) on the amount of radiation received. The developed method has been applied to several typical melt seasons (January–July during 2003, 2004, 2005, and 2009) over the western part of the United States, and the available information was used to derive metrics on spatial and temporal variability of shortwave fluxes. Issues of scale in both the satellite and ground observations are also addressed to illuminate difficulties in the validation process of satellite-derived quantities. It is planned to apply the findings from this study to test improvements in estimation of snow water equivalent.

Increasing warming of steadily shrinking Dead Sea surface water compensates for surface water cooling (due to increasing evaporation) and even causes observed positive Dead Sea sea surface temperature trends. This warming is caused by two factors: increasing daytime heat flow from land to sea (as a result of the steady shrinking) and regional atmospheric warming. Using observations from the Moderate Resolution Imaging Spectroradiometer (MODIS), positive trends were detected in both daytime and nighttime Dead Sea sea surface temperature (SST) over the period of 2000–2016. These positive SST trends were observed in the absence of positive trends in surface solar radiation, measured by the Dead Sea buoy pyranometer. We also show that long-term changes in water mixing in the uppermost layer of the Dead Sea under strong winds could not explain the observed SST trends. There is a positive feedback loop between the positive SST trends and the steady shrinking of the Dead Sea, which contributes to the accelerating decrease in Dead Sea water levels during the period under study. Satellite-based SST measurements showed that maximal SST trends of over 0.8 ∘C decade−1 were observed over the northwestern and southern sides of the Dead Sea, where shrinking of the Dead Sea water area was pronounced. No noticeable SST trends were observed over the eastern side of the lake, where shrinking of the Dead Sea water area was insignificant. This finding demonstrates correspondence between the positive SST trends and the shrinking of the Dead Sea indicating a causal link between them. There are two opposite processes taking place in the Dead Sea: sea surface warming and cooling. On the one hand, the positive feedback loop leading to sea surface warming every year accompanied by long-term increase in SST; on the other hand, the measured acceleration of the Dead Sea water-level drop suggests a long-term increase in Dead Sea evaporation accompanied by a long-term decrease in SST. During the period under investigation, the total result of these two opposite processes is the statistically significant positive sea surface temperature trends in both daytime (0.6 ∘C decade−1) and nighttime (0.4 ∘C decade−1), observed by the MODIS instrument. Our findings of the existence of a positive feedback loop between the positive SST trends and the shrinking of the Dead Sea imply the following significant point: any meteorological, hydrological or geophysical process causing the steady shrinking of the Dead Sea will contribute to positive trends in SST. Our results shed light on continuing hazards to the Dead Sea.

Precipitable water vapor (PWV) is an important climate parameter indicative of available moisture in the atmosphere; it is also an important greenhouse gas. Observations of precipitable water vapor in sub-Sahel West Africa are almost non-existent. Several Aerosol Robotic Network (AERONET) sites have been established across West Africa, and observations from four of them, namely, Ilorin (4.34° E, 8.32° N), Cinzana (5.93° W, 13.28° N), Banizoumbou (2.67° E, 13.54° N) and Dakar (16.96° W, 14.39° N) are being used in this study. Data spanning the period from 2004 to 2014 have been selected; they include conventional humidity parameters, remotely sensed aerosol and precipitable water information and numerical model outputs. Since in Africa, only conventional information on humidity parameters is available, it is important to utilize the unique observations from the AERONET network to calibrate empirical formulas frequently used to estimate precipitable water vapor from humidity measurements. An empirical formula of the form P W V = a T d + b where T d is the surface dew point temperature, a and b are constants, was fitted to the data and is proposed as applicable to the climatic condition of the sub-Sahel. Moreover, we have also used the AERONET information to evaluate the capabilities of well-established numerical weather prediction (NWP) models such as ERA Interim Reanalysis, NCEP-DOE Reanalysis II and NCEP-CFSR, to estimate precipitable water vapor in the sub-Sahel West Africa; it was found that the models tend to overestimate the amount of precipitable water at the selected sites by about 25 %.

For over a decade, several research groups have been developing air-sea heat flux information over the global ocean, including latent (LHF) and sensible (SHF) heat fluxes over the global ocean. This paper aims to provide new insight into the quality and error characteristics of turbulent heat flux estimates at various spatial and temporal scales (from daily upwards). The study is performed within the European Space Agency (ESA) Ocean Heat Flux (OHF) project. One of the main objectives of the OHF project is to meet the recommendations and requirements expressed by various international programs such as the World Research Climate Program (WCRP) and Climate and Ocean Variability, Predictability, and Change (CLIVAR), recognizing the need for better characterization of existing flux errors with respect to the input bulk variables (e.g. surface wind, air and sea surface temperatures, air and surface specific humidities), and to the atmospheric and oceanic conditions (e.g. wind conditions and sea state). The analysis is based on the use of daily averaged LHF and SHF and the asso- ciated bulk variables derived from major satellite-based and atmospheric reanalysis products. Inter-comparisons of heat flux products indicate that all of them exhibit similar space and time patterns. However, they also reveal significant differences in magnitude in some specific regions such as the western ocean boundaries during the Northern Hemisphere winter season, and the high southern latitudes. The differences tend to be closely related to large differences in surface wind speed and/or specific air humidity (for LHF) and to air and sea temperature differences (for SHF). Further quality investigations are performed through comprehensive comparisons with daily-averaged LHF and SHF estimated from moorings. The resulting statistics are used to assess the error of each OHF product. Consideration of error correlation between products and observations (e.g., by their assimilation) is also given. This reveals generally high noise variance in all products and a weak signal in common with in situ observations, with some products only slightly better than others. The OHF LHF and SHF products, and their associated error characteristics, are used to compute daily OHF multiproduct-ensemble (OHF/MPE) estimates of LHF and SHF over the ice-free global ocean on a 0.25° × 0.25° grid. The accuracy of this heat multiproduct, determined from comparisons with mooring data, is greater than for any individual product. It is used as a reference for the anomaly characterization of each individual OHF product.

In the long term, precipitation in the Central U.S. decreases by 25% during the seasonal transition from June to July. This precipitation decrease has intensified since 1979 and such intensification could have enhanced spring drought occurrences in the Central U.S., in which conditions quickly evolve from being abnormally dry to exceptionally dry. Various atmospheric and land reanalysis data sets were analyzed to examine the trend in the June–July seasonal transition. The intensified deficit in precipitation is accompanied by increased downward shortwave radiation flux, tropospheric subsidence, enhanced evaporative fraction, and elevated planetary boundary layer height, all of which can lead to surface drying. The change in tropospheric circulation was characterized by an anomalous ridge over the western U.S. and a trough on either side—a pattern known to suppress rainfall in the Central U.S. This trending pattern shows similarity with the progression of the 2012 record drought.

The Arctic is experiencing an unprecedented increase in surface air temperature and decrease in sea ice extent. The causes of these changes are still being debated; radiative fluxes are believed to play an important role in this warming. The primary motivation for this study is to advance the quality and resolution of currently available information on surface shortwave (solar) irradiance (SWR) for the Arctic. Such information is needed to meet the challenge for accurate estimates of heat input into the open waters. An inference scheme that utilizes the Moderate Resolution Imaging Spectroradiometer (MODIS) observations is optimized for high latitudes and implemented at 5 km for 2007 at an hourly time scale. Evaluation of the 5 km based SWR estimates against hourly ground observations at Barrow site shows a mean bias of 7.9 W m−2 (3% of mean values), a standard deviation of 58.2 W m−2 (23% of mean value), and a high correlation of 0.95. Evaluation of the SWR estimates against daily ground measurements at these latitudes shows good agreement with surface observations at three sites, with a mean bias of 1.9 W m−2 (1.1% of mean values), a standard deviation of 31.5 W m−2 (17.8% of mean value), and a high correlation of 0.96. Information at this high resolution and good quality can lead to improved estimates of heat input into the complex Arctic domain. For the Beaufort Sea domain (70°N–80°N, 120°E–50°E), the differences can amount to 116 MJ m−2 (~7%) of the total solar input of this region.