Estimation of 1-km Resolution All-Sky Instantaneous Erythemal UV-B with MODIS Data Based on a Deep Learning Method

Although ultraviolet-B (UV-B) radiation reaching the ground represents a tiny fraction of the total solar radiant energy, it significantly affects human health and global ecosystems. Therefore, erythemal UV-B monitoring has recently attracted significant attention. However, traditional UV-B retrieval methods rely on empirical modeling and handcrafted features, which require expertise and fail to generalize to new environments. Furthermore, most traditional products have low spatial resolution. To address this, we propose a deep learning framework for retrieving all-sky, kilometer-level erythemal UV-B from Moderate Resolution Imaging Spectroradiometer (MODIS) data. We designed a deep neural network with a residual structure to cascade high-level representations from raw MODIS inputs, eliminating handcrafted features. We used an external random forest classifier to perform the final prediction based on refined deep features extracted from the residual network. Compared with basic parameters, extracted deep features more accurately bridge the semantic gap between the raw MODIS inputs, improving retrieval accuracy. We established a dataset from 7 Surface Radiation Budget Network (SURFRAD) stations and 1 from 30 UV-B Monitoring and Research Program (UVMRP) stations with MODIS top-of-atmosphere reflectance, solar and view zenith angle, surface reflectance, altitude, and ozone observations. A partial SURFRAD dataset from 2007–2016 trained the model, achieving an R2 of 0.9887, a mean bias error (MBE) of 0.19 mW/m2, and a root mean square error (RMSE) of 7.42 mW/m2. The model evaluated on 2017 SURFRAD data shows an R2 of 0.9376, an MBE of 1.24 mW/m2, and an RMSE of 17.45 mW/m2, indicating the proposed model accurately generalizes the temporal dimension. We evaluated the model at 30 UVMRP stations with different land cover from those of SURFRAD and found most stations had a relative RMSE of 25% and an MBE within ±5%, demonstrating generalization in the spatial dimension. This study demonstrates the potential of using MODIS data to accurately estimate all-sky erythemal UV-B with the proposed algorithm.

Updated GOES-13 Heliosat-2 Method for Global Horizontal Irradiation in the Americas

Increasing the utilization of renewable energy is at the center of most sustainability policies. Solar energy is the most abundant resource of this type on Earth, and optimizing its use requires the optimal estimation of surface solar irradiation. Heliosat-2 is one of the most popular methods of global horizontal irradiation (GHI) estimation. Originally developed for the Meteosat satellite, Heliosat-2 has been modified in previous work to deal with GOES-13 data and named here GOES_H2. This model has been validated through the computation of indicators and irradiation maps for the Guiana Shield. This article proposes an improved version of GOES_H2, which has been combined with a radiative transfer parameterization (RTP) and the McClear clear-sky model (MC). This new version, hereafter designated RTP_MC_GOES_H2, was tested on eight stations from the Baseline Surface Radiation Network, located in North and South America, and covered by GOES-13. RTP_MC_GOES_H2 improves the hourly GHI estimates independently of the type of sky. This improvement is independent of the climate, no matter the station, the RTP_MC_GOES_H2 gives better results of MBE and RMSE than the original GOES_H2 method. Indeed, the MBE and RMSE values, respectively, change from −11.93% to −2.42% and 23.24% to 18.24% for North America and from −4.35% to 1.79% and 19.97% to 17.37 for South America. Moreover, the flexibility of the method may allow to improve results in the presence of snow cover and rainy/variable weather. Furthermore, RTP_MC_GOES_H2 results outperform or equalize those of other operational models.

Route measurements of natural surface radiation background in the Almaty region

Measurements of the spatial distribution of radon isotopes were carried out from April 2021 to August 2021 in the foothills of the Trans-Ili Alatau of the Tien Shan in the Almaty region at various heights above sea level: from 500 to 2500 meters. They were carried out using electronic radiometric equipment: beta-dosimeter “RKS-01B-SOLO”; gamma dosimeter “RKS-01G-SOLO”; radiometer of radon and its daughter decay products “RAMON- 02” in the field. As a result, preliminary assessment schemes were built for route measurements of the 222Rn radon isotope, beta and gamma radiation fields from natural daughter products of decay of radon isotopes and radionuclides located in the surface atmospheric layer.

Pulse Peak Migration during the Outburst Decay of the Magnetar SGR 1830-0645: Crustal Motion and Magnetospheric Untwisting

Magnetars, isolated neutron stars with magnetic-field strengths typically ≳1014 G, exhibit distinctive months-long outburst epochs during which strong evolution of soft X-ray pulse profiles, along with nonthermal magnetospheric emission components, is often observed. Using near-daily NICER observations of the magnetar SGR 1830-0645 during the first 37 days of a recent outburst decay, a pulse peak migration in phase is clearly observed, transforming the pulse shape from an initially triple-peaked to a single-peaked profile. Such peak merging has not been seen before for a magnetar. Our high-resolution phase-resolved spectroscopic analysis reveals no significant evolution of temperature despite the complex initial pulse shape, yet the inferred surface hot spots shrink during peak migration and outburst decay. We suggest two possible origins for this evolution. For internal heating of the surface, tectonic motion of the crust may be its underlying cause. The inferred speed of this crustal motion is ≲100 m day−1, constraining the density of the driving region to ρ ∼ 1010 g cm−3, at a depth of ∼200 m. Alternatively, the hot spots could be heated by particle bombardment from a twisted magnetosphere possessing flux tubes or ropes, somewhat resembling solar coronal loops, that untwist and dissipate on the 30–40 day timescale. The peak migration may then be due to a combination of field-line footpoint motion (necessarily driven by crustal motion) and evolving surface radiation beaming. This novel data set paints a vivid picture of the dynamics associated with magnetar outbursts, yet it also highlights the need for a more generic theoretical picture where magnetosphere and crust are considered in tandem.

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

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

SARAH-3 – a new satellite-based Climate Data Record of Surface Solar Radiation parameters

<p class="western"><span lang="en-US">The EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF) generates and distributes high quality long-term climate data records (CDR) of energy and water cycle parameters, which are freely available.</span></p> <p class="western"><span lang="en-US">In 2022, a new version of the “Surface Solar Radiation data set – Heliosat” will be released: SARAH-3. As the previous editions, the SARAH-3 climate data record is based on satellite observations from the first and second METEOSAT generations and provides various surface radiation parameters, including global radiation, direct radiation, sunshine duration, photosynthetic active radiation and others. SARAH-3 covers the time period 1983 to 2020 and offers 30-minute instantaneous data as well as daily and monthly means on a regular 0.05° x 0.05° lon/lat grid.</span></p> <p class="western" align="left"><span lang="en-US">In this presentation, an overview of the SARAH climate data record and their applications will be given. A focus will be on the SARAH-3 developments and validation with surface reference observations. Further, SARAH-3 will be used for a first analysis of the climate variability and potential trends of global radiation in Europe during the last decades. </span><span lang="en-US">The data record reveals that there is an increasing trend of surface solar radiation in Europe during the last decades, which is superimposed by decadal and regional variability.</span></p>

Integration of Sentinel-3 and MODIS Vegetation Indices with ERA-5 Agro-meteorological Indicators for Operational Crop Yield Forecasting

Timely crop yield forecasts at national level are substantial to support food policies, to assess agricultural production and to subsidize regions affected by food shortage. This study presents an operational crop yield forecasting system for Poland that employs freely available satellite and agro-meteorological products provided by the Copernicus programme. The crop yield predictors consist of: (1) vegetation condition indicators provided daily by Sentinel-3 OLCI (optical) and SLSTR (thermal) imagery, (2) a backward extension of Sentinel-3 data (before 2018) derived from cross-calibrated MODIS data, (3) air temperature, total precipitation, surface radiation, and soil moisture derived from ERA-5 climate reanalysis generated by the European Centre for Medium-Range Weather Forecasts. The crop yield forecasting algorithm is based on thermal time (growing degree days derived from ERA-5 data) to better follow the crop development stage. The recursive feature elimination is used to derive an optimal set of predictors for each administrative unit, which are ultimately employed by the Extreme Gradient Boosting regressor to forecast yields using official yield statistics as a reference. According to intensive leave-one-year-out cross validation for 2000–2019 period, the relative RMSE for NUTS-2 units are: 8% for winter wheat, and 13% for winter rapeseed and maize. Respectively, for the LAU units it equals 14% for winter wheat, 19% for winter rapeseed, and 27% for maize. The system is designed to be easily applicable in other regions and to be easily adaptable to cloud computing environments (such as DIAS or Amazon AWS), where data sets from the Copernicus programme are directly accessible.