Sea ice-atmosphere interaction. Application of multispectral satellite data in polar surface energy flux estimates

Satellite data for the estimation of radiative and turbulent heat fluxes is becoming an increasingly important tool in large-scale studies of climate. One parameter needed in the estimation of these fluxes is surface temperature. To our knowledge, little effort has been directed to the retrieval of the sea ice surface temperature (IST) in the Arctic, an area where the first effects of a changing climate are expected to be seen. The reason is not one of methodology, but rather our limited knowledge of atmospheric temperature, humidity, and aerosol profiles, the microphysical properties of polar clouds, and the spectral characteristics of the wide variety of surface types found there. We have developed a means to correct for the atmospheric attenuation of satellite-measured clear sky brightness temperatures used in the retrieval of ice surface temperature from the split-window thermal channels of the advanced very high resolution radiometer (AVHRR) sensors on-board three of the NOAA series satellites. These corrections are specified for three different 'seasons' and as a function of satellite viewing angle, and are expected to be applicable to the perennial ice pack in the central Arctic Basin

Surface radiation budget for climate applications

The Surface Radiation Budget (SRB) consists of the upwelling and downwelling radiation fluxes at the surface, separately determined for the broadband shortwave (SW) (0 to 5 micron) and longwave (LW) (greater than 5 microns) spectral regions plus certain key parameters that control these fluxes, specifically, SW albedo, LW emissivity, and surface temperature. The uses and requirements for SRB data, critical assessment of current capabilities for producing these data, and directions for future research are presented

McClear: a new model estimating downwelling solar radiation at ground level in clear-sky conditions

International audienceA new fast clear-sky model called McClear was developed to estimate the downwelling shortwave direct and global irradiances received at ground level under clear skies. It is a fully physical model replacing empirical relations or simpler models used before. It exploits the recent results on aerosol properties, and total column content in water vapour and ozone produced by the MACC project (Monitoring Atmosphere Composition and Climate). It accurately reproduces the irradiance computed by the libRadtran reference radiative transfer model with a computational speed approximately 105 times greater by adopting the abaci, or look-up table, approach combined with interpolation functions. It is therefore suited for geostationary satellite retrievals or numerical weather prediction schemes with many pixels or grid points, respectively. McClear irradiances were compared to 1 min measurements made in clear-sky conditions at several stations within the Baseline Surface Radiation Network in various climates. The bias for global irradiance comprises between −6 and 25Wm−2. The RMSE ranges from 20Wm−2 (3% of the mean observed irradiance) to 36Wm−2 (5 %) and the correlation coefficient ranges between 0.95 and 0.99. The bias for the direct irradiance comprises between −48 and +33Wm−2. The root mean square error (RMSE) ranges from 33Wm−2 (5 %) to 64Wm−2 (10 %). The correlation coefficient ranges between 0.84 and 0.98. This work demonstrates the quality of the McClear model combined with MACC products, and indirectly the quality of the aerosol properties modelled by the MACC reanalysis

Air–Sea Interaction in the Central Mediterranean Sea: Assessment of Reanalysis and Satellite Observations

Air–sea heat fluxes are essential climate variables, required for understanding air–sea interactions, local, regional and global climate, the hydrological cycle and atmospheric and oceanic circulation. In situ measurements of fluxes over the ocean are sparse and model reanalysis and satellite data can provide estimates at different scales. The accuracy of such estimates is therefore essential to obtain a reliable description of the occurring phenomena and changes. In this work, air–sea radiative fluxes derived from the SEVIRI sensor onboard the MSG satellite and from ERA5 reanalysis have been compared to direct high quality measurements performed over a complete annual cycle at the ENEA oceanographic observatory, near the island of Lampedusa in the Central Mediterranean Sea. Our analysis reveals that satellite derived products overestimate in situ direct observations of the downwelling short-wave (bias of 6.1 W/m2) and longwave (bias of 6.6 W/m2) irradiances. ERA5 reanalysis data show a negligible positive bias (+1.0 W/m2) for the shortwave irradiance and a large negative bias (−17 W/m2) for the longwave irradiance with respect to in situ observations. ERA5 meteorological variables, which are needed to calculate the air–sea heat flux using bulk formulae, have been compared with in situ measurements made at the oceanographic observatory. The two meteorological datasets show a very good agreement, with some underestimate of the wind speed by ERA5 for high wind conditions. We investigated the impact of different determinations of heat fluxes on the near surface sea temperature (1 m depth), as determined by calculations with a one-dimensional numerical model, the General Ocean Turbulence Model (GOTM). The sensitivity of the model to the different forcing was measured in terms of differences with respect to in situ temperature measurements made during the period under investigation. All simulations reproduced the true seasonal cycle and all high frequency variabilities. The best results on the overall seasonal cycle were obtained when using meteorological variables in the bulk formulae formulations used by the model itself. The derived overall annual net heat flux values were between +1.6 and 40.4 W/m2, depending on the used dataset. The large variability obtained with different datasets suggests that current determinations of the heat flux components and, in particular, of the longwave irradiance, need to be improved. The ENEA oceanographic observatory provides a complete, long-term, high resolution time series of high quality in situ observations. In the future, more similar sites worldwide will be needed for model and satellite validations and to improve the determination of the air–sea exchange and the understanding of related processes

Global Spatial and Temporal Variation of the Combined Effect of Aerosol and Water Vapour on Solar Radiation

This study aims to calculate the combined and individual effects of the optical thickness of aerosols (AOT) and precipitable water vapour (PWV) on the solar radiation reaching the Earth’s surface at a global scale and to analyse its spatial and temporal variation. For that purpose, a novel but validated methodology is applied to CERES SYN1deg products for the period 2000–2019. Spatial distributions of AOT and PWV effects, both individually and combined, show a close link with the spatial distributions of AOT and PWV. The spatially averaged combined effect results in a −13.9% reduction in irradiance, while the average AOT effect is −2.3%, and the PWV effect is −12.1%. The temporal analysis focuses on detecting trends in the anomalies. The results show overall positive trends for AOT and PWV. Consequently, significant negative overall trends are found for the effects. However, significant positive trends for the individual AOT and the combined AOT-PWV effects are found in specific regions, such as the eastern United States, Europe or Asia, indicating successful emission control policies in these areas. This study contributes to a better understanding of the individual and combined effects of aerosols and water vapour on solar radiation at a global scale

Daily grass reference evapotranspiration with Meteosat Second Generation shortwave radiation and reference ET products

This study assesses the accuracy of estimating daily grass reference evapotranspiration (PM-ETo) using daily shortwave radiation (Rs) and reference evapotranspiration (ETREF) products provided by the Meteosat Second Generation (MSG) geostationary satellite delivered by the Satellite Applications Facility on Land Surface Analysis (LSA-SAF) framework. The accuracy of using reanalysis ERA5 shortwave radiation data (Rs ERA5) provided by the European Center for Medium-Range Weather Forecasts (ECMWF) is also evaluated. The assessments were performed using observed weather variables at 37 weather stations distributed across continental Portugal, where climate conditions range from semi-arid to humid, and 12 weather stations located in Azores islands, characterized by humid, windy and often cloudy conditions. This study’s use of data from a variety of climate conditions contributed to a unique and innovative assessment of the usability of LSA-SAF and ERA5 products for ETo estimation. The first assessment focused on comparing LSA-SAF estimates of Rs (Rs LSA-SAF) against ground stations (Rs ground). The results showed a good matching between the two Rs data sets for continental Portugal but a tendency for Rs LSA-SAF to under-estimate Rs ground in the cloudy islands of Azores. ETo values computed using Rs LSA-SAF data and observed temperature, humidity and wind speed (ETo LSA-SAF) were then compared with PMETo estimates with ground-based data, which were used as benchmark; input data of temperature and humidity needed for PM-ETo were quality checked for surface aridity effects. It was observed that ETo LSA-SAF is strongly correlated with PM-ETo (R2 > 0.97) for most locations in continental Portugal, with regression coefficient of a linear regression forced to the origin ranging between 0.95 and 1.05, mean root mean square error (RMSE) of 0.13 mm d 1, and Nash and Sutcliff efficiency of modeling (EF) above 0.95. For most Azores locations, ETo LSA-SAF over-estimated PM-ETo. This is likely a consequence of the high spatio-temporal heterogeneity of weather conditions that occur in these oceanic islands together with the different footprints of satellite (averaged over the pixel) and station observations. Reanalysis ERA5 shortwave radiation data presented similar behavior to the LSA-SAF products, however with slightly lower accuracy. The daily LSA-SAF ETREF product (ETREF LSA-SAF) was assessed and results have shown a good accuracy of this product, with acceptable RMSE and high EF values, for continental Portugal but a low accuracy for the Azores islands. A simplified bias correction approach was shown to improve both ETo derived from the LSA-SAF products, namely for Azores stations, which seem to be representative of smaller areas. The use of the FAO-PM temperature approach (PMT) was also assessed using the Rs LSA-SAF and Rs ERA5 data, which showed a superiority of the LSA-SAF product for ETo estimations (ETo PMT LSA-SAF). No significant differences (p < 0.05) were observed in terms of the median value of the RMSE when adopting ETo PMT and ETREF LSA-SAF. Differently, results showed that using the Rs LSA-SAF in the PMT approach (ETo PMT LSA-SAF) produces significantly better RMSE results than ETo PMT and ETREF LSA-SAF. Overall, the performed assessment allows concluding that the use of Rs LSA-SAF, and to a lesser extent the use of the Rs ERA5, highly improves the accuracy of computation of ETo when Rs observations are not available, including when only temperature data are accessible. The use of the ETREF LSA-SAF product is a good alternative when observed weather data are not availableinfo:eu-repo/semantics/publishedVersio

An Efficient Method of Estimating Downward Solar Radiation Based on the MODIS Observations for the Use of Land Surface Modeling

Solar radiation is a critical variable in global change sciences. While most of the current global datasets provide only the total downward solar radiation, we aim to develop a method to estimate the downward global land surface solar radiation and its partitioned direct and diffuse components, which provide the necessary key meteorological inputs for most land surface models. We developed a simple satellite-based computing scheme to enable fast and reliable estimation of these variables. The global Moderate Resolution Imaging Spectroradiometer (MODIS) products at 1° spatial resolution for the period 2003–2011 were used as the forcing data. Evaluations at Baseline Surface Radiation Network (BSRN) sites show good agreement between the estimated radiation and ground-based observations. At all the 48 BSRN sites, the RMSE between the observations and estimations are 34.59, 41.98 and 28.06 W∙m−2 for total, direct and diffuse solar radiation, respectively. Our estimations tend to slightly overestimate the total and diffuse but underestimate the direct solar radiation. The errors may be related to the simple model structure and error of the input data. Our estimation is also comparable to the Clouds and Earth’s Radiant Energy System (CERES) data while shows notable improvement over the widely used National Centers for Environmental Prediction and National Center for Atmospheric Research (NCEP/NCAR) Reanalysis data. Using our MODIS-based datasets of total solar radiation and its partitioned components to drive land surface models should improve simulations of global dynamics of water, carbon and climate

ESTIMATING SURFACE LONGWAVE RADIATION AND APPLICATIONS TO HIGH LATITUDE ISSUES

Two models, with distinct advantages for calculating downwelling surface longwave (DSLW) radiation under all sky conditions are presented. Both models are driven with a combination of Moderate Resolution Imaging Spectroradiometer (MODIS) level-3 cloud parameters and information from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim model. To compute the clear sky component of DSLW the first model DSLW/UMD v1 utilizes a globally applicable parameterization. The second generation model DSLW/UMD v2 utilizes a two layer feed-forward artificial neural network with sigmoid hidden neurons and linear output neurons. When computing the cloud contribution to DSLW, DSLW/UMD v1 implements a commonly used statistical model to calculate cloud vertical height while in DSLW/UMD v2 the cloud base temperature is estimated by using an independent artificial neural network based on spatially and temporally co- located MODIS and Cloudsat Cloud Profiling Radar (CPR) and the Cloud-Aerosol Lidar and Infrared Pathfiner Satellite Observation (CALIPSO) Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) observations. Daily average estimates of DSLW for 2003 to 2009 are compared against ground measurements from the Baseline Surface Radiation Network (BSRN) and show significant improvements over currently available model estimates. DSLW/UMD v2 as optimized for Polar Regions along with a UMD develop shortwave model are used to investigate the role of radiative components in Arctic sea ice anomalies. The correlation between downwelling surface longwave and shortwave radiation and sea ice anomaly for the period from 2003 to 2007 is investigated using the latest Moderate Resolution Imagining Spectroradiometer (MODIS) level-3 cloud parameters and information from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim model. All sky downwelling surface longwave radiation (DSLW), all sky downwelling shortwave radiation (DSSW), all sky total downwelling shortwave and longwave radiation (DSSW + DSLW), and cloud total cloud forcing are individually examined to determine their respective correlation to sea ice anomaly. It is determined that these radiation components are not the primary drivers for major sea ice anomalies that occur during the investigated time frame within the 120o E to 210o E region

An Evaluation of Unmanned Aerial System Multispectral and Thermal Infrared Data as Information for Agricultural Crop and Irrigation Management

Spatial irrigation management has been steadily advancing over the last several years. A current issue with managing irrigation spatially on sub-field scale is the inability to readily collect the spatial field data necessary to properly manage irrigation. Multispectral and thermal infrared imagery used in informing irrigation management decisions was previously collected by satellite and manned aircraft remote sensing platforms. These remote sensing platforms pose issues concerning economic feasibility, revisit intervals, and weather factors that inhibit the collection of data. Recent developments in unmanned aerial systems, which provide an additional means of collecting multispectral and thermal infrared data, have the potential to provide supplemental data during periods of missing satellite data or to completely replace satellite and manned aircraft remote sensing platforms. As unmanned aerial system remote sensing platforms are a relatively new technology, there are uncertainties regarding how these systems compare to previous and more well-known remote sensing platforms. Some of these uncertainties include how to properly collect, process, and calibrate data acquired by these systems so that the end products are accurate and can by used in scientific applications. This work evaluated two different unmanned aerial systems with integrated multispectral and thermal infrared cameras to determine the best methods of collecting, processing, and calibrating data. Three different multispectral image calibration methods were evaluated and compared against Landsat satellite reflectance products and ground-based reflectance tarps. The thermal infrared image calibration consisted of correcting for emissivity and atmospheric effects, and was compared to in-field infrared thermometers. Relationships for estimating maize leaf area index, crop height, and fraction of vegetation cover were redefined and evaluated based on various vegetation indices derived from the unmanned aerial system calibrated multispectral imagery. This work also addressed some of the challenges and obstacles related to deploying unmanned aerial systems for remote sensing in agricultural applications. Advisors: Wayne E. Woldt and Christopher M.U. Neal