Modern-Era Retrospective Analysis For Research And Applications Research Articles

Global solar radiation predictions in Fiji Islands based on empirical models

Many difficulties associated with Global Solar Radiation (GSR)’ variability in space and time and with the angular position in sky hemisphere hamper its accurate knowledge. Fiji Islands, with a tropical maritime climate without great extremes of heat or cold, have GSR measurement limited by a small number of stations due to the technical and financial restricts. Yet there are various options of available empirical models to select the most suitable one(s) for estimating GSR accurately using weather-related variables available for each location. This paper, therefore, focuses on evaluation of the performances of twenty (20) empirical models in predicting GSR between 1984 and 2018 in six (6) meteorological stations within Fiji Islands. Simulations were performed for 35-year period (1984–2018) and results were compared with the National Aeronautics and Space Administration (NASA)’s MERRA (Modern-Era Retrospective Analysis for Research and Applications) observational datasets after bias corrections. Model performance indices such as Mean Error (ME), Nash-Sutcliffe Efficiency coefficient (NSE), Percentage Mean Error (PME), Root Mean Square Error (RMSE) and correlation coefficient (r) were adopted. Obtained coefficients of determination from the regression analysis (0.415 ≤ R2 ≤ 0.988; at 95% confidential level) performed for the models’ calibration between 1984 and 2018 suggest that the models’ potentials to simulate GSR varies across the stations and among the models. About 33% of the models in group I, which relate GSR to the sunshine duration, monthly average day length and/or extra-terrestrial radiation performed favourably well in predicting GSR while 75% of models in group II (with air temperature as additional predictor) and 100% in group III (humidity) performed very well. It is concluded that inclusion of air temperature and humidity as two principal predictors alongside with sunshine duration, day length and extra-terrestrial radiation produce the best GSR prediction (RMSE ≤ 5 MJ m−2) over Fiji Islands.

Suitability of different precipitation data sources for hydrological analysis: a study from Western Ghats, India.

For hydrological analysis, it is essential to have continuous and long-term precipitation data. However, the precipitation data from rain gauge stations are often insufficient and not continuous. At present, ground-based gridded data and satellite-based gridded data are often used as an alternative. However, these data sets have to be evaluated for their suitability in hydrological studies. The current study compared three different rainfall data sources with the observed station data for the Kallada River basin of Kerala, India. The ground-based gridded rainfall data from the India Meteorological Department (IMD), the high-resolution satellite product Tropical Rainfall Measuring Mission (TRMM 3B43, version 7), and the reanalysis data Modern-Era Retrospective Analysis for Research and Applications (MERRA) are used in the analysis. The correlation coefficient, normalized root mean square error, Nash-Sutcliffe efficiency, modified index of agreement, and volumetric efficiency are used as performance indicators. The performance indicator's weights are based on the entropy method. The multi-criteria decision-making techniques like compromise programming and Preference Ranking Organization Method (PROMETHEE II) are used for ranking the precipitation data sources. It is found that IMD ground-based gridded data is ranked 1 among the three data sets. The IMD ground-based gridded data are not homogeneous based on the absolute homogeneity test, even though they had the highest rank. The IMD gridded data are further corrected based on double mass curve analysis. The corrected data were analyzed using the precipitation concentration index (PCI) to assess the temporal variation in precipitation, and it was found that the location falls under a uniform distribution zone.

Weather pattern associated with climate change during Canadian Arctic wildfires: A case study in July 2019

Wildfire is one of the major novel disturbances to the Arctic forest ecosystem. Relevant Weather and climate regimes are the most important elements affecting fire activity. However, these factors could be considered under changing due to Arctic warming. This paper discusses understanding relevant atmospheric weather and climate conditions over western Canada in July 2019 with a significant burned area fraction over western Canada, Alaska, and Siberia. Here, we show that daytime area-averaged Methane mole fraction for the month of July during recent two decades obtained from Modern-Era Retrospective Analysis for Research and Applications (MERRA) database has significant growth trend over Siberia-Alaska and western Canada with the highest value for the west part of Canada in July 2019. Also, the Pearson correlation coefficient displays the strong negative relationship between north hemisphere sea ice extent and daytime areas averaged Methane mole fraction during the month of July for Siberia-Alaska and western Canada at −0.72 and −0.69 correspondingly. However, during the last two decades, the average Carbone monoxide (CO) column burden for these areas has been slightly decreased. This research demonstrates that the Arctic region has faced an albedo decrease with a maximum value of around 40% departure from mean values in some parts mostly over the northern Canadian Arctic and northern Alaska boundary for this time. Monthly weather and climate map analysis have revealed that Canadian July 2019 wildfires were linked to different factors including the rather lower humidity (with a maximum of 17.5% in North Territories) and higher surface air temperatures. Besides, the composite mean weather map analysis for the middle troposphere shows strong climate–fire links, with effects of the local blocking anti-cyclonic high-pressure systems dominated over the western Canadian Arctic, Alaska, Siberia (mostly over the south and central parts). Daily weather analysis for the peak day, July 24 reveals a tilted mid-tropospheric long-wave ridge formation accompanies by a very dry surface air (relative humidity ∼30-% 40) and warm tongue extended from the northwest of the United State to western Canada and Alaska. During the wildfire case of July 24, 2019, south-westerlies to south easterlies winds caused the fire to spread mostly over western and southwestern areas in the local afternoon time.

Assessment of satellite-based MERRA climate data in AASHTOWare pavement mechanistic-empirical design

The pavement performance system depends on various factors including traffic loading, climate conditions, and material properties. Variations in ambient temperatures and moistures can change material properties and consequently affect the overall pavement performance. Collecting climate data has been a significant challenge. This study compares the pavement performance predictions of Iowa composite pavement systems in AASHTOWare Pavement ME Design (PMED) software using four climate data sources: (1) ground-based weather station, (2) North American Regional Reanalysis and (3) the Modern-Era Retrospective Analysis for Research and Applications (MERRA) version 1 and (4) MERRA version 2. The findings showed disagreement among climate data sources for some of the distresses due to the significant differences in percent sunshine hourly measurements. Database for ‘synthetic’ percent sunshine is developed using surface shortwave radiation (SSR) estimates directly provided by MERRA to improve pavement performance predictions. This study recommends the use of SSR as an input in PMED.

Impact of Different Wind Representations on Resonant Ocean Near-inertial Motions in the Gulf of Mexico

This study investigates the resonated diurnal impact of the surface wind forcing from several atmospheric wind products, including CCMP (Cross-Calibrated Multi-Platform), ERA Interim, NCEP-2 (NCEP-DOE AMIP-II Reanalysis), MERRA (Modern-Era Retrospective analysis for Research and Application), NARR (Northern American Regional Reanalysis) and NAM (North American Mesoscale Forecast System), on the wind-driven near-inertial ocean motions in the northern Gulf of Mexico by numerical simulation, and in comparison, with NDBC buoy surface observed winds. Our analyses show that the near-inertial wind power input, which can affect vertical mixing in the water column, is closely associated with the variability of land-sea breeze by various wind products. In comparison with buoy observations in the northern Gulf of Mexico, the MERRA has the worst land-sea breeze representation, while the NCEP2 and CCMP, which are chosen to represent the coarsest and medium–high horizontal resolution, have the best performance. We further show that by comparison of the buoy data, that the near-inertial wind power input derived from all the wind products is underestimated by 30–50% approximately.

Seasonal forecasts of the Saharan heat low characteristics: a multi-model assessment

Abstract. The Saharan heat low (SHL) is a key component of the West African Monsoon system at the synoptic scale and a driver of summertime precipitation over the Sahel region. Therefore, accurate seasonal precipitation forecasts rely in part on a proper representation of the SHL characteristics in seasonal forecast models. This is investigated using the latest versions of two seasonal forecast systems namely the SEAS5 and MF7 systems from the European Center of Medium-Range Weather Forecasts (ECMWF) and Météo-France respectively. The SHL characteristics in the seasonal forecast models are assessed based on a comparison with the fifth ECMWF Reanalysis (ERA5) for the period 1993–2016. The analysis of the modes of variability shows that the seasonal forecast models have issues with the timing and the intensity of the SHL pulsations when compared to ERA5. SEAS5 and MF7 show a cool bias centered on the Sahara and a warm bias located in the eastern part of the Sahara respectively. Both models tend to underestimate the interannual variability in the SHL. Large discrepancies are found in the representation of extremes SHL events in the seasonal forecast models. These results are not linked to our choice of ERA5 as a reference, for we show robust coherence and high correlation between ERA5 and the Modern-Era Retrospective analysis for Research and Applications (MERRA). The use of statistical bias correction methods significantly reduces the bias in the seasonal forecast models and improves the yearly distribution of the SHL and the forecast scores. The results highlight the capacity of the models to represent the intraseasonal pulsations (the so-called east–west phases) of the SHL. We notice an overestimation of the occurrence of the SHL east phases in the models (SEAS5, MF7), while the SHL west phases are much better represented in MF7. In spite of an improvement in prediction score, the SHL-related forecast skills of the seasonal forecast models remain weak for specific variations for lead times beyond 1 month, requiring some adaptations. Moreover, the models show predictive skills at an intraseasonal timescale for shorter lead times.

Assessment of EnKF data assimilation of satellite-derived soil moisture over the Indian domain with the Noah land surface model

Land surface models (LSMs) are typically forced with observed precipitation and surface meteorology and hence the soil moisture estimates obtained from LSM do not reflect the contribution of irrigation to the soil moisture estimates. However, the satellite retrievals of soil moisture estimates do register the signature of the irrigation effects. It is suggested that the soil moisture estimates obtained from LSM may reflect the role of irrigation if they are assimilated with soil moisture estimated from satellites. The present study evaluates the improvement of soil moisture estimates obtained from Noah LSM by ingesting them with the satellite-derived Advanced Scatterometer (ASCAT) soil moisture retrievals over the Indian domain for the year 2012. The above ingesting of soil moisture estimates is performed using the land information system (LIS). The improved soil moisture estimates are validated with the in situ India Meteorological Department (IMD) soil moisture observations and also with the high-resolution Indian Monsoon Data Assimilation and Analysis (IMDAA) regional reanalysis data. The percentages of grid points over the Indian domain where the improvement parameter shows positive values are 59.14% (winter), 69.17% (pre-monsoon), 43.59% (monsoon), and 77.53% (post-monsoon). Furthermore, the forecast impact parameter also indicates the positive impact of data assimilation. Also, 12 of the 22 stations show reduced RMSE soil moisture error after data assimilation is performed while only 6 of the 22 stations show higher correlation coefficient in soil moisture without data assimilation, when validated with the in situ IMD soil moisture observations. The study has also evaluated the irrigation impact of ASCAT in the assimilated soil moisture using triple collocation (TC) method. For the TC analysis, the model-based Global Land Data Assimilation System (GLDAS), Catchment Land Surface Model (CLSM), and MERRA (Modern-Era Retrospective analysis for Research and Applications) Land data set together with soil moisture model outputs with and without ASCAT assimilation are used to calculate the error and correlation coefficient of each of the two set of triplets. The results of the TC analysis further conclusively show the positive impact of irrigation effects in the ASCAT-assimilated soil moisture model output.

PBL Height From AIRS, GPS RO, and MERRA‐2 Products in NASA GES DISC and Their 10‐Year Seasonal Mean Intercomparison

AbstractWithin the planetary boundary layer (PBL), surface forcing response, drag, turbulence, and vertical mixing are important processes and play a more critical role here than in the overlying “free atmosphere.” The PBL height (PBLH) is an important parameter in climate models, weather forecasts, and air quality prediction. The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) provides data processing, archiving, and distribution services for numerous Earth science products. PBLH is a parameter in three products served by GES DISC, which are from the atmospheric infrared sounder (AIRS), the global positioning system (GPS) radio occultation (RO) experiment, and the NASA reanalysis product Modern‐Era Retrospective analysis for Research and Applications‐2 (MERRA‐2). These products have different spatial and temporal resolutions and coverages, and their PBLH definitions are also different. To better serve the PBL research community, we have summarized the specifications of these products. A 10‐year seasonal mean intercomparison is also conducted to provide further guidance to users. The intercomparison results show that MERRA‐2 has a much shallower PBL than AIRS and GPS RO. An experimental study indicates that the different PBLH definition in MERRA‐2 caused smaller values of PBLH. The improvement of the water vapor retrieval in AIRS version 7 over version 6 results in the version 7 PBLH agreeing better with GPS RO and MERRA‐2 than version 6, especially near the equator and low latitudes.

Potential health and economic benefits of air quality improvements in Morocco: A simulation based on particles during the COVID19 lockdown

BACKGROUND AND AIM: Several measures have been taken to mitigate the effects of the COVID19 pandemic. In this context, almost all non-essential activities in Morocco have been halted since March 20, 2020. The implementation of these measures has led to a notable improvement in air quality. The main objective of this study is to estimate economic and health benefits caused by the reduction in fine particulate matter (PM2.5) concentrations during the COVID19 scenario in Morocco. METHODS: This study presents the spatio-temporal evolution of the observed concentrations of PM2.5 by the Modern-Era Retrospective analysis for Research and Applications (MERRA) during the periods 2017-2019 and 2020. Then, these observed concentrations were compared to those predicted by Copernicus Atmosphere Monitoring Service (CAMS). In addition, the health and the economic benefits caused by PM2.5 during the COVID19 lockdown in Morocco were estimated using the Environmental Benefits Mapping and Analysis Program (BenMAP). RESULTS:According to this study, reducing the concentration of particles can prevent deaths from several diseases, namely pneumonia and asthma. Also, owing to the reduction in air pollution, especially particulate matter, millions of dollars per year were estimated as an economic benefit arising during the study period in Morocco. CONCLUSIONS:This hypothetical clean air scenario has shown the significant health and economic benefits that could be achieved through transport, climate and environmental strategies and policies that support a robust economic recovery and reduced emissions at the same time. KEYWORDS: Air pollution, particulate matter, Health co-benefits, COVID19

Large Contributions of Diffuse Radiation to Global Gross Primary Productivity During 1981–2015

AbstractDiffuse radiation can increase the light use efficiency for plant photosynthesis. However, the lack of observations limits the explorations of diffuse fertilization effects on the global scale. Here, we bias‐correct global hourly diffuse fraction (Kd) from a climate reanalysis using an artificial neural network (ANN) model in combination with site‐level observations. Evaluations at independent sites show that the updated Kd on average increases correlations by 9.7% and reduces root‐mean‐square errors (RMSEs) by 45.5% against measurements than the original reanalysis. The derived radiative fluxes are then used as input for a dynamic vegetation model to explore the impacts of diffuse radiation on global gross primary productivity (GPP) during 1981–2015. With the updated Kd, simulated GPP shows lower RMSEs against observations at 72 out of 76 FLUXNET sites, leading to a reduced RMSE of 9.9% on average. Moreover, simulations with updated Kd present higher global GPP (3.1%) than that with original Kd from Modern‐Era Retrospective Analysis for Research and Applications (MERRA) product. The simulations show that diffuse radiation, which accounts for 54% of the total shortwave radiation, contributes to long‐term global mean GPP by 1.49 g C m−2 day−1 (64.3%). During 1981–2015, the changes of direct radiation result in a global GPP trend of −0.49 g C m−2 yr−2, which is offset by 0.31 g C m−2 yr−2 (53.1%) following the enhancement of diffuse radiation.

The retrieval of snow properties from SLSTR Sentinel-3 – Part 2: Results and validation

Abstract. To evaluate the performance of the eXtensible Bremen Aerosol/cloud and surfacE parameters Retrieval (XBAER) algorithm, presented in the Part 1 companion paper to this paper, we apply the XBAER algorithm to the Sea and Land Surface Temperature Radiometer (SLSTR) instrument on board Sentinel-3. Snow properties – snow grain size (SGS), snow particle shape (SPS) and specific surface area (SSA) – are derived under cloud-free conditions. XBAER-derived snow properties are compared to other existing satellite products and validated by ground-based and aircraft measurements. The atmospheric correction is performed on SLSTR for cloud-free scenarios using Modern-Era Retrospective Analysis for Research and Applications (MERRA) aerosol optical thickness (AOT) and the aerosol typing strategy according to the standard XBAER algorithm. The optimal SGS and SPS are estimated iteratively utilizing a look-up-table (LUT) approach, minimizing the difference between SLSTR-observed and SCIATRAN-simulated surface directional reflectances at 0.55 and 1.6 µm. The SSA is derived for a retrieved SGS and SPS pair. XBAER-derived SGS, SPS and SSA have been validated using in situ measurements from the recent campaign SnowEx17 during February 2017. The comparison shows a relative difference between the XBAER-derived SGS and SnowEx17-measured SGS of less than 4 %. The difference between the XBAER-derived SSA and SnowEx17-measured SSA is 2.7 m2/kg. XBAER-derived SPS can be reasonably explained by the SnowEx17-observed snow particle shapes. Intensive validation shows that (1) for SGS and SSA, XBAER-derived results show high correlation with field-based measurements, with correlation coefficients higher than 0.85. The root mean square errors (RMSEs) of SGS and SSA are around 12 µm and 6 m2/kg. (2) For SPS, aggregate SPS retrieved by XBAER algorithm is likely to be matched with rounded grains while single SPS in XBAER is possibly linked to faceted crystals. The comparison with aircraft measurements, during the Polar Airborne Measurements and Arctic Regional Climate Model Simulation Project (PAMARCMiP) campaign held in March 2018, also shows good agreement (with R=0.82 and R=0.81 for SGS and SSA, respectively). XBAER-derived SGS and SSA reveal the variability in the aircraft track of the PAMARCMiP campaign. The comparison between XBAER-derived SGS results and the Moderate Resolution Imaging Spectroradiometer (MODIS) Snow-Covered Area and Grain size (MODSCAG) product over Greenland shows similar spatial distributions. The geographic distribution of XBAER-derived SPS over Greenland and the whole Arctic can be reasonably explained by campaign-based and laboratory investigations, indicating a reasonable retrieval accuracy of the retrieved SPS. The geographic variabilities in XBAER-derived SGS and SSA both over Greenland and Arctic-wide agree with the snow metamorphism process.

Techno-Economic Investigation of Wind Energy Potential in Selected Sites with Uncertainty Factors

This work demonstrates a techno-economical assessment of wind energy potential for four passes of Tamil Nadu (Aralvaimozhi, Shencottah, Palghat, and Cumbum) with uncertainty factors. First, a potential assessment was carried out with time-series data, and the Weibull parameters, such as c (scale) and k (shape), were determined using the modern-era retrospective analysis for research and applications (MEERA) data set. Using these parameters, the mean speed, most probable speed, power density, maximum energy-carrying speed of wind power were determined. From the analysis, it was observed that all four passes had better wind parameters; notably, the Aralvaimozhi pass attained a better range of about 6.563 m/s (mean wind speed), 226 W/m2 (wind power density), 6.403 m/s (most probable wind speed), and 8.699 m/s (max wind speed). Further, uncertainty factors, such as the probability of exceedance (PoE), wind shear co-efficient (WSC), surface roughness, and wake loss effect (WLE), were evaluated. The value of PoE was found to be within the bound for all the locations, i.e., below 15%. In addition, the ranged of WSC showed a good trend between 0.05 and 0.5. Moreover, the surface length of the passes was evaluated and recorded to be 0.0024 m with a 73% energy index. Further, output power, annual energy production (AEP), capacity factor (CF), and cost of wind energy of all four passes were computed using different wind turbine ratings in two cases, i.e., with and without WLE. It was observed that there was a huge profit in loss from all the four locations due to WLE that was estimated to be Rupees (Rs.) 10.07 crores without considering interest components and Rs. 13.66 crores with interest component at a 10% annual rate of interest.

Diurnal Variability of Surface Temperature over Lakes: Case Study for Lake Huron

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).

Simulation of radon-222 with the GEOS-Chem global model: emissions, seasonality, and convective transport

Abstract. Radon-222 (222Rn) is a short-lived radioactive gas naturally emitted from land surfaces and has long been used to assess convective transport in atmospheric models. In this study, we simulate 222Rn using the GEOS-Chem chemical transport model to improve our understanding of 222Rn emissions and surface concentration seasonality and characterize convective transport associated with two Goddard Earth Observing System (GEOS) meteorological products, the Modern-Era Retrospective analysis for Research and Applications (MERRA) and GEOS Forward Processing (GEOS-FP). We evaluate four global 222Rn emission scenarios by comparing model results with observations at 51 surface sites. The default emission scenario in GEOS-Chem yields a moderate agreement with surface observations globally (68.9 % of data within a factor of 2) and a large underestimate of winter surface 222Rn concentrations at Northern Hemisphere midlatitudes and high latitudes due to an oversimplified formulation of 222Rn emission fluxes (1 atom cm−2 s−1 over land with a reduction by a factor of 3 under freezing conditions). We compose a new global 222Rn emission scenario based on Zhang et al. (2011) and demonstrate its potential to improve simulated surface 222Rn concentrations and seasonality. The regional components of this scenario include spatially and temporally varying emission fluxes derived from previous measurements of soil radium content and soil exhalation models, which are key factors in determining 222Rn emission flux rates. However, large model underestimates of surface 222Rn concentrations still exist in Asia, suggesting unusually high regional 222Rn emissions. We therefore propose a conservative upscaling factor of 1.2 for 222Rn emission fluxes in China, which was also constrained by observed deposition fluxes of 210Pb (a progeny of 222Rn). With this modification, the model shows better agreement with observations in Europe and North America (> 80 % of data within a factor of 2) and reasonable agreement in Asia (close to 70 %). Further constraints on 222Rn emissions would require additional concentration and emission flux observations in the central United States, Canada, Africa, and Asia. We also compare and assess convective transport in model simulations driven by MERRA and GEOS-FP using observed 222Rn vertical profiles in northern midlatitude summer and from three short-term airborne campaigns. While simulations with both GEOS products are able to capture the observed vertical gradient of 222Rn concentrations in the lower troposphere (0–4 km), neither correctly represents the level of convective detrainment, resulting in biases in the middle and upper troposphere. Compared with GEOS-FP, MERRA leads to stronger convective transport of 222Rn, which is partially compensated for by its weaker large-scale vertical advection, resulting in similar global vertical distributions of 222Rn concentrations between the two simulations. This has important implications for using chemical transport models to interpret the transport of other trace species when these GEOS products are used as driving meteorology.

Assessment of Remote Sensing and Re-Analysis Estimates of Regional Precipitation over Mato Grosso, Brazil

The spatial and temporal distribution of precipitation is of great importance for the rain-fed agricultural production and the socioeconomics of Mato Grosso (MT), Brazil. MT has a sparse network of ground rain gauges that limits the effective use of precipitation information for sustainable agricultural production and water resources in the region. Several gridded precipitation products from remote sensing and reanalysis of land surface models are currently available that can enhance the use of such information. However, these products are available at different spatial and temporal resolutions which add some challenges to stakeholders (users) to identify their appropriateness for specific applications (e.g., irrigation requirements, length of growing season, and drought monitoring). Thus, it is necessary to provide an assessment of the reliability of these precipitation estimates. The objective of this work was to compare regional precipitation estimates over MT as provided by the Global Land Data Assimilation (GLDAS), Modern-Era Retrospective Analysis for Research and Applications (MERRA), Tropical Rainfall Measurement Mission (TRMM), Global Precipitation Measurement (GPM), and the Global Precipitation Climatology Project (GPCP) with ground-based measurements. The comparison was conducted for the 2000–2018 period at eleven ground-based weather stations that covered different climate zones in MT using daily, monthly, and annual temporal resolutions. The comparison used the Pearson correlation index–r, Willmott index–d, root mean square error—RMSE, and the Wilks methods. The results showed GPM and GLDAS estimates did not differ significantly with the measured daily, monthly, and annual precipitation. TRMM estimates slightly overestimated daily precipitation by about 4.7% but did not show significant difference on the monthly and annual scales when compared with local measurements. The GPCP underestimated annual precipitation by about 7.1%. MERRA underestimated daily, monthly, and annual precipitation by about 22.9% on average. In general, all products satisfactorily estimated monthly precipitation, and most of them satisfactorily estimated annual precipitation; however, they showed low accuracy when estimating daily precipitation. The TRMM, GPM, GPCP, and GLDAS estimates had the highest performance, from high to low, while MERRA showed the lowest performance. The findings of this study can be used to support the decision-making process in the region in application related to water resources management, sustainability of agriculture production, and drought management.

Temperature gradient method for deriving planetary boundary layer height from AIRS profile data over the Heihe River Basin of China

The planetary boundary layer (PBL) or atmospheric boundary layer (ABL) refers to the part of the atmosphere that is directly influenced by the Earth’s surface and responds to surface forces. In this paper, the temperature gradient method that uses the Atmospheric Infrared Sounder (AIRS) atmospheric potential temperature profile product to derive the planetary boundary layer height (PBLH) is introduced and applied over the Heihe River Basin (HRB). The method is applicable during clear and cloudy days for both convective and stable air conditions. The reanalysis PBLH from the Modern Era Retrospective analysis for Research and Applications (MERRA) data and the in situ PBLH calculated from HIWATER (Heihe Watershed Allied Telemetry Experimental Research) radiosonde data were both prepared for validation. The comparison shows that the spatial variation in the PBLH from the AIRS is consistent with the MERRA PBLH at the annual scale, and the PBLH from our method has an average of 313 m RMSE and 0.84 R2 with the station in situ PBLH calculated from sounding data. The study demonstrates that remote sensing methodologies can successfully estimate the MH under different atmosphere condition without the need for ancillary field measurements.

Global and regional variations in aerosol loading during COVID-19 imposed lockdown

In the backdrop of upward trend in anthropogenic aerosols over global hotspot regions, the air quality had improved worldwide post declaration of the Corona virus disease-2019 (COVID-19) as a global pandemic in mid-March-2020. Present study using Moderate Resolution Imaging Spectroradiometer (MODIS) satellite derived aerosol optical depth (AOD) and the Modern-Era Retrospective analysis for Research and Applications (MERRA) version-2 datasets however, demonstrates the regional variation in aerosol loading during peak of the lockdown period. Reduction in aerosol loading over majority of the aerosol hotspots is observed from mid-March/April-2020 with highest percentage reduction in the month of May. Reduction in aerosol loading over global hotspots resulted in positive surface aerosol radiative forcing (ARF, up to 6 Wm-2). Albeit reduction in aerosol loading observed worldwide, the considerable above normal aerosol burden was identified during April–May 2020 over the Amazon river basin, northern parts of the South America, Mexico region, South-West parts of the Africa and South East Asian region. Analysis revealed that the wildfire emission contributed significantly in anomalous aerosol burden over these regions during the lockdown period. An appropriate mitigation measures to reduce wildfire emissions is essential in addition to controlled anthropogenic emissions as far as air quality, deforestation and ecosystem is concerned.

Impact of Meteorological Changes on Particulate Matter and Aerosol Optical Depth in Seoul during the Months of June over Recent Decades

The effects of meteorological changes on particulate matter with a diameter of 10 microns or less (PM10, referred to as PM in this study) and aerosol optical depth (AOD) in Seoul were investigated using observational and modeling analysis. AOD satellite data were used, obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS), and PM concentration data were used from in-situ observations. The Modern-Era Retrospective Analysis for Research and Applications (MERRA) and MERRA Version 2 (MERRA-2) were used for meteorological field analysis in modeling and observation data. The results from this investigation show that meteorological effects on PM and AOD were strong in the month of June, revealing a clear decreasing trend in recent decades. The investigation focused on the underlying mechanisms influencing the reduction in PM resulting from meteorological changes during the months of June. The results of this study reveal that decreases in atmospheric stability and humidity induced the aerosol change observed in recent decades. The changes in atmospheric stability and humidity are highly correlated with changes in the intensity of the East Asian summer monsoon (EASM). This suggests that the unstable and drying atmosphere by weakening of the EASM in recent decades has improved PM air quality in Seoul during the summer. The effects of atmospheric stability and humidity were also observed to vary depending on the aerosol species. Humidity only affects hydrophilic aerosols such as sulfate, nitrate, and ammonium, whereas atmospheric stability affects all species of aerosols, including carbonaceous aerosols.

Validation of Reanalysis-Based Offshore Wind Resource Characterization Using Lidar Buoy Observations

AbstractThe offshore wind industry in the United States is gaining strong momentum to achieve sustainable energy goals, and the need for observations to provide resource characterization and model validation is greater than ever. Pacific Northwest National Laboratory (PNNL) operates two lidar buoys for the U.S. Department of Energy (DOE) in order to collect hub height wind data and associated meteorological and oceanographic information near the surface in areas of interest for offshore wind development. This work evaluates the performance of commonly used reanalysis products and spatial approximation techniques using lidar buoy observations off the coast of New Jersey and Virginia, USA. Reanalysis products are essential tools for setting performance expectations and quantifying the wind resource variability at a given site. Long-term accurate observations at typical wind turbine hub heights have been lacking at offshore locations. Using wind speed observations from both lidar buoy deployments, biases and degrees of correspondence for the Modern-Era Retrospective Analysis for Research and Applications-2 (MERRA-2), the North American Regional Reanalysis (NARR), ERA5, and the analysis system of the Rapid Refresh (RAP) are examined both at hub height and near the surface. Results provide insights on the performance and uncertainty of using reanalysis products for long-term wind resource characterization. A slow bias is seen across the reanalyses at both deployment sites. Bias magnitudes near the surface are on the order of 0.5 m s−1 greater than their hub height counterparts. RAP and ERA5 produce the highest correlations with the observations, around 0.9, followed by MERRA-2 and NARR.

Insight into the precipitation behavior of gridded precipitation data in the Sina basin.

This paper examines the performance of three gridded precipitation data sets, namely, Global Precipitation Climatology Centre (GPCC), Tropical Precipitation Measuring Mission (TRMM), and the Modern-Era Retrospective Analysis for Research and Applications (MERRA), for the duration of 25years using 9 rain gauge data sets of the Sina basin, India. Statistical measures were employed to measure the performance in reproducing the rainfall and to assess its ability to detect the rainfall/no rainfall events, its structure, pattern, and spatio-temporal variations in the monthly and annual time scales. Compromise programming (CP) is used to rank the statistical performances of selected gridded precipitation data sets and found that TRMM attained first rank for the 8 stations followed by MERRA. The precipitation concentration index (PCI) checks the pattern and distribution of rainfall and found that observed data shows a uniform distribution in the basin; however, all the three gridded data sets failed to demonstrate uniform distribution. Categorical metrics like Probability of Detection (POD) and False Alarm Ratio (FAR) revealed that TRMM followed by MERRA and GPCC have good capabilities to detect rainfall/no rainfall events at different thresholds. All the trends drawn between observed data set and gridded precipitation data sets revealed that the MERRA data tend to underestimate and the TRMM and GPCC data tend to overestimate the values and intensities of rainfall data sets at most of the stations for both monthly and annual time scales. The data analysis of extreme rainfall points at monthly and annual time scales exhibits better performance of TRMM data sets. Overall, the TRMM data set is capable in replicating different characteristics of the observed data in the study area and could be used for hydro-meteorological and climatic studies when continuous observed data set is not available.