ground measurements

Aerosol emissions from biomass burning are of specific interest over the globe due to their strong radiative impacts and climate implications. The present study examines the impact of paddy crop residue burning over northern India during the postmonsoon (October-November) season of 2012 on modification of aerosol properties, as well as the long-range transport of smoke plumes, altitude characteristics, and affected areas via the synergy of ground-based measurements and satellite observations. During this period, Moderate Resolution Imaging Spectroradiometer (MODIS) images show a thick smoke/hazy aerosol layer below 2-2.5 km in the atmosphere covering nearly the whole Indo-Gangetic Plains (IGP). The air mass trajectories originating from the biomass-burning source region over Punjab at 500 m reveal a potential aerosol transport pathway along the Ganges valley from west to east, resulting in a strong aerosol optical depth (AOD) gradient. Sometimes, depending upon the wind direction and meteorological conditions, the plumes also influence central India, the Arabian Sea, and the Bay of Bengal, thus contributing to Asian pollution outflow. The increased number of fire counts (Terra and Aqua MODIS data) is associated with severe aerosol-laden atmospheres (AOD 500 nm > 1.0) over six IGP locations, high values of Ångström exponent (>1.2), high particulate mass 2.5 (PM 2.5 ) concentrations (>100-150 μgm À3 ), and enhanced Ozone Monitoring Instrument Aerosol Index gradient (~2.5) and NO 2 concentrations (~6 × 10 15 mol/cm 2 ), indicating the dominance of smoke aerosols from agricultural crop residue burning. The aerosol size distribution is shifted toward the fine-mode fraction, also exhibiting an increase in the radius of fine aerosols due to coagulation processes in a highly turbid environment. The spectral variation of the single-scattering albedo reveals enhanced dominance of moderately absorbing aerosols, while the aerosol properties, modification, and mixing atmospheric processes differentiate along the IGP sites depending on the distance from the aerosol source, urban influence, and local characteristics. Local and regional meteorology (air temperature, wind speed, and prolonged droughts) plays a crucial role in the ignition and spread of forest fires, while agricultural burning is commonly practiced by the farmers in developing countries to improve the nutrition level in the soil and agricultural productivity. In Asia, three types KASKAOUTIS ET AL.

The present study focuses on analyzing the precipitation trends over six Indian cities during the summer monsoon (June–September) covering the period 1951–2007 and also attempting to investigate possible urban forcing and dynamics by examining the variation in precipitation in the upwind and downwind directions. The analysis shows negative trends in the total number of rainy days over Hyderabad (−10.4%), Kanpur (−7.1%), Jaipur (−10.5%), and Nagpur (−4.8%) and positive trends over Delhi (7.4%) and Bangalore (22.9%). On the other hand, decreases of −21.3%, −5.9%, −14.2%, and −14.6% in seasonal rainfall are found over Delhi, Hyderabad, Jaipur, and Kanpur, respectively, whereas Bangalore and Nagpur show 65.8% and 13.5% increase. The lesser rainfall and rainy days, along with the mostly declining trend, in the downwind directions of the cities may imply an urban influence in precipitation associated with the increased anthropogenic emissions due to expansion of the urban areas and the in...

Air pollution is a leading global disease risk factor. Tracking progress (e.g. for Sustainable Development Goals) requires accurate, spatially resolved, routinely updated exposure estimates. A Bayesian Hierarchical Model was developed to estimate annual average fine particle (PM2.5) concentrations at 0.1° × 0.1° spatial resolution globally for 2010-2016. The model incorporated spatially-varying relationships between 6003 ground measurements from 117 countries, satellite-based estimates and other predictors. Model coefficients indicated larger contributions from satellite-based estimates in countries with low monitor density. Within and out-of-sample cross-validation indicated improved predictions of ground measurements compared to previous (Global Burden of Disease 2013) estimates (increased within-sample R2 from 0.64 to 0.91, reduced out-of-sample, global population-weighted Root Mean Squared Error from 23 μg/m3 to 12 μg/m3). In 2016, 95% of the world's population lived in area...

This communication assesses six methods estimating UltraViolet A and B (UV-A and UV-B) fluxes from satellite imagery, numerical weather models or ground measurements. The UV estimates from each method are compared to coincident 15 minutes in-situ measurements collected at one location in Uruguay from September 2015 to January 2019. The first method "LAAM" (Locally-Adapted Antón Martínez) combines Global Horizontal Irradiance (G) measured on site with satellite-derived daily Ozone concentration. The second method "Wald" uses an empirical model onto satellite-retrieved solar broadband irradiance at the surface (SSI) produced by HelioClim-3 version 5 (HC3v5) to derive UV fluxes. The third method named "CAMS-UV", is one of the outputs of ECMWF numerical weather model. The three remaining methods are respectively named "Weighted_Kato HC3v5", "Discretized _Kato HC3v5" and "DWD SARAH-3". They rely on more sophisticated modelling of the atmosphere in cloud-free conditions using radiative transfer modelling combined to a cloud modification factor (or cloud extinction) derived from HC3v5. Outside an underestimation observed for the UV-B range for both CAMS-UV (-20 %) and for the empirical model (-29 %), methods demonstrated their ability to collect the temporal variability of the signal of the instrument on-ground; biases ranges from-2 to 4 % for UV-A and from 0 to 10 % for UV-B, RMSE are close to 15 % and almost all correlation coefficients exceed 0.96. This analysis gives precious elements for discussion about the performance of models mainly developed and validated over Europe and Africa.

In addition to technical issues related to the instruments used, differences between soil moisture (SM) measured using ground-based methods and microwave remote sensing (RS) can be related to the main features of the study areas, which are intricately connected to hydraulic–hydrological conditions and soil properties. When long-term analysis is performed, these discrepancies are mitigated by the contribution of SM seasonality and are only evident when high-frequency variations (i.e., signal anomalies) are investigated. This study sought to examine the responsiveness of SM to seasonal variations in terrestrial ecoregions located in areas covered by the in situ Romanian Soil Moisture Network (RSMN). To achieve this aim, several remote sensing-derived retrievals were considered: (i) NASA’s Soil Moisture Active and Passive (SMAP) L4 V5 model assimilated product data; (ii) the European Space Agency’s Soil Moisture and Ocean Salinity INRA–CESBIO (SMOS-IC) V2.0 data; (iii) time-series data extracted from the H115 and H116 SM products, which are derived from the analysis of Advanced Scatterometer (ASCAT) data acquired via MetOp satellites; (iv) Copernicus Global Land Service SSM 1 km data; and (v) the “combined” European Space Agency’s Climate Change Initiative for Soil Moisture (ESA CCI SM) product v06.1. An initial assessment of the performance of these products was conducted by checking the anomaly of long-term fluctuations, quantified using the Absolute Variation of Local Change of Environment (ALICE) index, within a time frame spanning 2015 to 2020. These correlations were then compared with those based on raw data and anomalies computed using a moving window of 35 days. Prominent correlations were observed with the SMAP L4 dataset and across all ecoregions, and the Balkan mixed forests (646) exhibited strong concordance regardless of the satellite source (with a correlation coefficient RALICE > 0.5). In contrast, neither the Central European mixed forests (No. 654) nor the Pontic steppe (No. 735) were adequately characterized by any satellite dataset (RALICE < 0.5). Subsequently, the phenological seasonality and dynamic behavior of SM were computed to investigate the effects of the wetting and drying processes. Notably, the Central European mixed forests (654) underwent an extended dry phase (with an extremely low p-value of 2.20 × 10−16) during both the growth and dormancy phases. This finding explains why the RSMN showcases divergent behavior and underscores why no satellite dataset can effectively capture the complexities of the ecoregions covered by this in situ SM network.

This paper presents the ground soil moisture measurements performed over the socalled Gourma meso-scale site in Mali, Sahel, in the context of the African Monsoon Multidisciplinary Analysis (AMMA) project. The Gourma meso-scale soil moisture network is part of a complete land surface processes observing and modelling strategy and is associated to vegetation and meteorological field measurements as well as soil moisture remote sensing. It is spanning 2 • in latitude between 15 • N and 17 • N. In 2007, it includes 10 soil moisture stations, of which 3 stations also have meteorological and flux measurements. A relevant spatial sampling strategy is proposed to characterise soil moisture at different scales including local, kilometer, super-site and meso-scales. In addition to the local stations network, transect measurements were performed on different coarse textured (sand to sandy-loam) sites, using portable impedance probes. They indicate mean value and standard deviation (STD) of the

A field campaign was carried out to evaluate the Soil Moisture (SM) MIR_SMUDP2 product (v5.51) generated from the data of the Microwave Imaging Radiometer using Aperture Synthesis (MIRAS) aboard the Soil Moisture and Ocean Salinity (SMOS) mission. The study area was the Pampean Region of Argentina, which was selected because it is a vast area of flatlands containing quite homogeneous rain-fed croplands, which are considered SMOS nominal land uses and hardly affected by radio-frequency interference contamination. Transects of ground handheld SM measurements were performed using ThetaProbe ML2x probes within four Icosahedral Snyder Equal Area Earth (ISEA) grid nodes, where permanent SM stations are located. The campaign results showed a negative bias of −0.02 m 3 m −3 between concurrent SMOS data and ground SM measurements, which means a slight SMOS underestimation, and a standard deviation of ±0.06 m 3 m −3. Additionally, a good correlation was obtained between the handheld SM measurements taken during the campaign and the permanent SM station data within a node, which pointed out that the station data could be used as reference data to evaluate the SMOS product over a longer temporal period. SMOS-retrieved data were also compared with station mean SM values from 2012 to 2014. A general SMOS underestimation of −0.05 m 3 m −3 was observed, with a standard deviation of

Resumen: La evapotranspiración (ET) es la variable hidrológica de mayor relevancia en la Región Pampeana Argentina (RPA) debido a que mediante este proceso se pierde gran parte del agua que ingresa como precipitación al sistema. Conocer la variabilidad espacial de la ET es esencial para introducirla en modelos hidrológicos y el uso de productos de satélite resulta una herramienta clave para ello. En este trabajo, se evaluó el producto MOD16A2 de ET potencial (ETp) (versión 6; MOD16A2.006) comparándolo con datos in situ de 24 estaciones distribuidas en la RPA para los años 2012, 2013 y 2014, observándose una sobreestimación mayor al 50%, determinando la existencia de un error sistemático. Debido a este hallazgo, se procedió a calibrar y posteriormente corregir el producto con una ecuación de ajuste lineal, logrando, de este modo, disminuir la diferencia cuadrática media, de 2,31 a 0,51 mm día-1 y el error medio absoluto de 2,08 a 0,37 mm día-1. Tras dicha corrección, el producto MOD16A2.006 mejora significativamente y puede utilizarse con fines hidrológicos a escala de cuenca. Se recomienda evaluar el producto en otras regiones del mundo para determinar si existen errores sistemáticos, y eventualmente corregirlos siguiendo la metodología aquí propuesta.

A field campaign was carried out to evaluate the Soil Moisture (SM) MIR_SMUDP2 product (v5.51) generated from the data of the Microwave Imaging Radiometer using Aperture Synthesis (MIRAS) aboard the Soil Moisture and Ocean Salinity (SMOS) mission. The study area was the Pampean Region of Argentina, which was selected because it is a vast area of flatlands containing quite homogeneous rain-fed croplands, which are considered SMOS nominal land uses and hardly affected by radio-frequency interference contamination. Transects of ground handheld SM measurements were performed using ThetaProbe ML2x probes within four Icosahedral Snyder Equal Area Earth (ISEA) grid nodes, where permanent SM stations are located. The campaign results showed a negative bias of −0.02 m 3 m −3 between concurrent SMOS data and ground SM measurements, which means a slight SMOS underestimation, and a standard deviation of ±0.06 m 3 m −3. Additionally, a good correlation was obtained between the handheld SM measurements taken during the campaign and the permanent SM station data within a node, which pointed out that the station data could be used as reference data to evaluate the SMOS product over a longer temporal period. SMOS-retrieved data were also compared with station mean SM values from 2012 to 2014. A general SMOS underestimation of −0.05 m 3 m −3 was observed, with a standard deviation of

The present study focuses on analyzing the precipitation trends over six Indian cities during the summer monsoon (June–September) covering the period 1951–2007 and also attempting to investigate possible urban forcing and dynamics by examining the variation in precipitation in the upwind and downwind directions. The analysis shows negative trends in the total number of rainy days over Hyderabad (−10.4%), Kanpur (−7.1%), Jaipur (−10.5%), and Nagpur (−4.8%) and positive trends over Delhi (7.4%) and Bangalore (22.9%). On the other hand, decreases of −21.3%, −5.9%, −14.2%, and −14.6% in seasonal rainfall are found over Delhi, Hyderabad, Jaipur, and Kanpur, respectively, whereas Bangalore and Nagpur show 65.8% and 13.5% increase. The lesser rainfall and rainy days, along with the mostly declining trend, in the downwind directions of the cities may imply an urban influence in precipitation associated with the increased anthropogenic emissions due to expansion of the urban areas and the in...

The Pampas Region is a big plain of approximately 520,000 km 2 in Argentina. It is essential to estimate evapotranspiration (ET) in this region since the primary productivity is directly linked to water availability. Information provided by satellite missions allows monitoring the spatial and temporal variability of ET. In the current study, we evaluated the version 006 of MOD16A2 product (MOD16A2.006) of Potential Evapotranspiration (ETp) and Actual Evapotranspiration (ETa) in Argentinian Pampas Region (APR). MOD16A2.006 product was compared with Crop Evapotranspiration (ETc), calculated with local measurements from the Oficina de Riesgo Agropecuario (ORA), and Crop Coefficient (Kc) data (function of Normalized Difference Vegetation Index (NDVI)) in seven stations in the APR from 2009 to 2018. We evaluated ETa at two temporal scales: accumulated values (mm) per growth stages (soybean crop), and 8-day accumulated values (mm8d À1). The results showed a systematic overestimation around 65% for ETp (MOD16A2.006) (found and eliminated by means of a linear function) and underestimation (in most stations) for ETa (MOD16A2.006) in accumulated values per growth stages. Respect to mm8d À1 , no systematic error was observed, but the relationship ETa (ORA) À ETa (MOD16A2.006) for soybean crop behaves similarly throughout APR.

The Pampas Region is a big plain of approximately 520,000 km 2 in Argentina. It is essential to estimate evapotranspiration (ET) in this region since the primary productivity is directly linked to water availability. Information provided by satellite missions allows monitoring the spatial and temporal variability of ET. In the current study, we evaluated the version 006 of MOD16A2 product (MOD16A2.006) of Potential Evapotranspiration (ETp) and Actual Evapotranspiration (ETa) in Argentinian Pampas Region (APR). MOD16A2.006 product was compared with Crop Evapotranspiration (ETc), calculated with local measurements from the Oficina de Riesgo Agropecuario (ORA), and Crop Coefficient (Kc) data (function of Normalized Difference Vegetation Index (NDVI)) in seven stations in the APR from 2009 to 2018. We evaluated ETa at two temporal scales: accumulated values (mm) per growth stages (soybean crop), and 8-day accumulated values (mm8d À1). The results showed a systematic overestimation around 65% for ETp (MOD16A2.006) (found and eliminated by means of a linear function) and underestimation (in most stations) for ETa (MOD16A2.006) in accumulated values per growth stages. Respect to mm8d À1 , no systematic error was observed, but the relationship ETa (ORA) À ETa (MOD16A2.006) for soybean crop behaves similarly throughout APR.

A field campaign was carried out to evaluate the Soil Moisture (SM) MIR_SMUDP2 product (v5.51) generated from the data of the Microwave Imaging Radiometer using Aperture Synthesis (MIRAS) aboard the Soil Moisture and Ocean Salinity (SMOS) mission. The study area was the Pampean Region of Argentina, which was selected because it is a vast area of flatlands containing quite homogeneous rain-fed croplands, which are considered SMOS nominal land uses and hardly affected by radio-frequency interference contamination. Transects of ground handheld SM measurements were performed using ThetaProbe ML2x probes within four Icosahedral Snyder Equal Area Earth (ISEA) grid nodes, where permanent SM stations are located. The campaign results showed a negative bias of −0.02 m 3 m −3 between concurrent SMOS data and ground SM measurements, which means a slight SMOS underestimation, and a standard deviation of ±0.06 m 3 m −3. Additionally, a good correlation was obtained between the handheld SM measurements taken during the campaign and the permanent SM station data within a node, which pointed out that the station data could be used as reference data to evaluate the SMOS product over a longer temporal period. SMOS-retrieved data were also compared with station mean SM values from 2012 to 2014. A general SMOS underestimation of −0.05 m 3 m −3 was observed, with a standard deviation of

The Pampas Region is a big plain of approximately 520,000 km 2 in Argentina. It is essential to estimate evapotranspiration (ET) in this region since the primary productivity is directly linked to water availability. Information provided by satellite missions allows monitoring the spatial and temporal variability of ET. In the current study, we evaluated the version 006 of MOD16A2 product (MOD16A2.006) of Potential Evapotranspiration (ETp) and Actual Evapotranspiration (ETa) in Argentinian Pampas Region (APR). MOD16A2.006 product was compared with Crop Evapotranspiration (ETc), calculated with local measurements from the Oficina de Riesgo Agropecuario (ORA), and Crop Coefficient (Kc) data (function of Normalized Difference Vegetation Index (NDVI)) in seven stations in the APR from 2009 to 2018. We evaluated ETa at two temporal scales: accumulated values (mm) per growth stages (soybean crop), and 8-day accumulated values (mm8d À1). The results showed a systematic overestimation around 65% for ETp (MOD16A2.006) (found and eliminated by means of a linear function) and underestimation (in most stations) for ETa (MOD16A2.006) in accumulated values per growth stages. Respect to mm8d À1 , no systematic error was observed, but the relationship ETa (ORA) À ETa (MOD16A2.006) for soybean crop behaves similarly throughout APR.

The Pampas Region is a big plain of approximately 520,000 km 2 in Argentina. It is essential to estimate evapotranspiration (ET) in this region since the primary productivity is directly linked to water availability. Information provided by satellite missions allows monitoring the spatial and temporal variability of ET. In the current study, we evaluated the version 006 of MOD16A2 product (MOD16A2.006) of Potential Evapotranspiration (ETp) and Actual Evapotranspiration (ETa) in Argentinian Pampas Region (APR). MOD16A2.006 product was compared with Crop Evapotranspiration (ETc), calculated with local measurements from the Oficina de Riesgo Agropecuario (ORA), and Crop Coefficient (Kc) data (function of Normalized Difference Vegetation Index (NDVI)) in seven stations in the APR from 2009 to 2018. We evaluated ETa at two temporal scales: accumulated values (mm) per growth stages (soybean crop), and 8-day accumulated values (mm8d À1). The results showed a systematic overestimation around 65% for ETp (MOD16A2.006) (found and eliminated by means of a linear function) and underestimation (in most stations) for ETa (MOD16A2.006) in accumulated values per growth stages. Respect to mm8d À1 , no systematic error was observed, but the relationship ETa (ORA) À ETa (MOD16A2.006) for soybean crop behaves similarly throughout APR.

The SMAP (soil moisture active passive) which is one of the satellites that specifically designed for soil moisture monitoring, was launched on 31 January 2015. Recently, the SMAP radiometer soil moisture product has been released to the public. It is very urgent to evaluate the reliability of this product before it can be widely used in hydrometeorological studies. In the study, we carried out an initial evaluation of SMAP radiometer soil moisture product against in-situ measurements from three networks. The three networks cover different land surface conditions, including two dense networks established in United States and Finland, and one sparse network set up in Romania. The results show that the SMAP soil moisture product agrees very well with the in-situ measurements although it sometimes exhibits dry or wet bias at different network regions. The overall ubRMSE of SMAP product is 0.036 m 3 m-3 , well within the mission requirement of 0.04 m 3 m-3 • Considering the algorithms are still under refinement, it can be reasonably expected that applications such as climate modeling and flood forecasting will benefit from the SMAP passive soil moisture product.