Accurate solar surface irradiance data is a prerequisite for an efficient planning and operation of solar energy systems. Further, it is essential for…Accurate solar surface irradiance data is a prerequisite for an efficient planning and operation of solar energy systems. Further, it is essential for climate monitoring and analysis. Recently, the demand on information about spectrally resolved solar surface irradiance has grown. As surface measurements are rare, satellite derived information with high accuracy might fill this gap. This paper describes a new approach for the retrieval of spectrally resolved solar surface irradiance from satellite data. The method combines a eigenvector-hybrid look-up table approach for the clear sky case with satellite derived cloud transmission (Heliosat method). The eigenvector LUT approach is already used to retrieve the broadband solar surface irradiance of data sets provided by the Climate Monitoring Satellite Application Facility (CM-SAF). This paper describes the extension of this approach to wavelength bands and the combination with spectrally resolved cloud transmission values derived with radiative transfer corrections of the broadband cloud transmission. Thus, the new approach is based on radiative transfer modeling and enables the use of extended information about the atmospheric state, among others, to resolve the effect of water vapor and ozone absorption bands. The method is validated with spectrally resolved measurements from two sites in Europe and by comparison with radiative transfer calculations. The validation results demonstrate the ability of the method to retrieve accurate spectrally resolved irradiance from satellites. The accuracy is in the range of the uncertainty of surface measurements, with exception of the UV and NIR (≥ 1200 nm) part of the spectrum, where higher deviations occur.more
Knowledge of the wintertime sea-ice production in Arctic polynyas is an important requirement for estimations of the dense water formation, which driv…Knowledge of the wintertime sea-ice production in Arctic polynyas is an important requirement for estimations of the dense water formation, which drives vertical mixing in the upper ocean. Satellite-based techniques incorporating relatively high resolution thermal-infrared data from MODIS in combination with atmospheric reanalysis data have proven to be a strong tool to monitor large and regularly forming polynyas and to resolve narrow thin-ice areas (i.e., leads) along the shelf-breaks and across the entire Arctic Ocean. However, the selection of the atmospheric data sets has a large influence on derived polynya characteristics due to their impact on the calculation of the heat loss to the atmosphere, which is determined by the local thin-ice thickness. In order to overcome this methodical ambiguity, we present a MODIS-assisted temperature adjustment (MATA) algorithm that yields corrections of the 2 m air temperature and hence decreases differences between the atmospheric input data sets. The adjustment algorithm is based on atmospheric model simulations. We focus on the Laptev Sea region for detailed case studies on the developed algorithm and present time series of polynya characteristics in the winter season 2019/2020. It shows that the application of the empirically derived correction decreases the difference between different utilized atmospheric products significantly from 49% to 23%. Additional filter strategies are applied that aim at increasing the capability to include leads in the quasi-daily and persistence-filtered thin-ice thickness composites. More generally, the winter of 2019/2020 features high polynya activity in the eastern Arctic and less activity in the Canadian Arctic Archipelago, presumably as a result of the particularly strong polar vortex in early 2020.more
We present a new database of solar radiation at ground level for Eastern Europe and Africa, the Middle East and Asia, estimated using satellite images…We present a new database of solar radiation at ground level for Eastern Europe and Africa, the Middle East and Asia, estimated using satellite images from the Meteosat East geostationary satellites. The method presented calculates global horizontal (G) and direct normal irradiance (DNI) at hourly intervals, using the full Meteosat archive from 1998 to present. Validation of the estimated global horizontal and direct normal irradiance values has been performed by comparison with high-quality ground station measurements. Due to the low number of ground measurements in the viewing area of the Meteosat Eastern satellites, the validation of the calculation method has been extended by a comparison of the estimated values derived from the same class of satellites but positioned at 0°E, where more ground stations are available. Results show a low overall mean bias deviation (MBD) of +1.63 Wm−2 or +0.73% for global horizontal irradiance. The mean absolute bias of the individual station MBD is 2.36%, while the root mean square deviation of the individual MBD values is 3.18%. For direct normal irradiance the corresponding values are overall MBD of +0.61 Wm−2 or +0.62%, while the mean absolute bias of the individual station MBD is 5.03% and the root mean square deviation of the individual MBD values is 6.30%. The resulting database of hourly solar radiation values will be made freely available. These data will also be integrated into the PVGIS web application to allow users to estimate the energy output of photovoltaic (PV) systems not only in Europe and Africa, but now also in Asia.more
Abstract
Global total column water vapor trends have been derived from both the Global Ozone Monitoring Experiment (GOME) and the Scanning…Abstract
Global total column water vapor trends have been derived from both the Global Ozone Monitoring Experiment (GOME) and the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) satellite data and from globally distributed radiosonde measurements, archived and quality controlled by the Deutscher Wetterdienst (DWD).
The control of atmospheric water vapor amount by the hydrological cycle plays an important role in determining surface temperature and its response to the increase in man-made greenhouse effect. As a result of its strong infrared absorption, water vapor is the most important naturally occurring greenhouse gas. Without water vapor, the earth surface temperature would be about 20 K lower, making the evolution of life, as we know it, impossible. The monitoring of water vapor and its evolution in time is therefore of utmost importance for our understanding of global climate change. Comparisons of trends derived from independent water vapor measurements from satellite and radiosondes facilitate the assessment of the significance of the observed changes in water vapor.
In this manuscript, the authors have compared observed water vapor change and trends, derived from independent instruments, and assessed the statistical significance of their differences. This study deals with an example of the Behrens–Fisher problem, namely, the comparison of samples with different means and different standard deviations, applied to trends from time series.
Initially the Behrens–Fisher problem for the derivation of the consolidated change and trends is solved using standard (frequentist) hypothesis testing by performing the Welch test. Second, a Bayesian model selection is applied to solve the Behrens–Fisher problem by integrating the posterior probabilities numerically by using the algorithm Differential Evolution Markov Chain (DEMC). Additionally, an analytical approximative solution of the Bayesian posterior probabilities is derived by means of a quadratic Taylor series expansion applied in a computationally efficient manner to large datasets. The two statistical methods used in the study yield similar results for the comparison of the water vapor changes and trends from the different measurements, yielding a consolidated and consistent behavior.more
A newly developed linear sea ice concentration (SIC) retrieval algorithm based on passive microwave Advanced Microwave Scanning Radiometer 2 (AMSR2) m…A newly developed linear sea ice concentration (SIC) retrieval algorithm based on passive microwave Advanced Microwave Scanning Radiometer 2 (AMSR2) measurements is proposed. SIC is retrieved by a linear function of the polarization ratio (PR) at 89 GHz (PR89) corrected for atmospheric influence. We use Landsat 8 SIC data to derive the coefficients of the linear function. Results using this linear algorithm are compared to those of ASI2 developed by Lu et al. (2018), which is a nonlinear 89-GHz algorithm with polarization difference (PD) at 89 GHz (PD89) that also includes a correction for atmospheric influence. Both algorithms are compared with independent SIC data derived from Landsat 8, ship-based observation, and synthetic aperture radar (SAR) and both tend to underestimate the ship-based and Landsat 8 SICs, particularly over thin ice. However, the proposed algorithm tends to provide results with lower bias and root-mean-square error (RMSE) for different ice categories.more
Abstract
Using a two-year dataset (2016–17) from 17 one-minute rain gauges located in the moist forest region of Ghana, the performance of…Abstract
Using a two-year dataset (2016–17) from 17 one-minute rain gauges located in the moist forest region of Ghana, the performance of Integrated Multisatellite Retrievals for GPM, version 6b (IMERG), is evaluated based on a subdaily time scale, down to the level of the underlying passive microwave (PMW) and infrared (IR) sources. Additionally, the spaceborne cloud product Cloud Property Dataset Using SEVIRI, edition 2 (CLAAS-2), available every 15 min, is used to link IMERG rainfall to cloud-top properties. Several important issues are identified: 1) IMERG’s proneness to low-intensity false alarms, accounting for more than a fifth of total rainfall; 2) IMERG’s overestimation of the rainfall amount from frequently occurring weak convective events, while that of relatively rare but strong mesoscale convective systems is underestimated, resulting in an error compensation; and 3) a decrease of skill during the little dry season in July and August, known to feature enhanced low-level cloudiness and warm rain. These findings are related to 1) a general oversensitivity for clouds with low ice and liquid water path and a particular oversensitivity for low cloud optical thickness, a problem which is slightly reduced for direct PMW overpasses; 2) a pronounced negative bias for high rain intensities, strongest when IR data are included; and 3) a large fraction of missed events linked with rainfall out of warm clouds, which are inherently misinterpreted by IMERG and its sources. This paper emphasizes the potential of validating spaceborne rainfall products with high-resolution rain gauges on a subdaily time scale, particularly for the understudied West African region.more
The soil moisture active/passive (SMAP) mission represents a significant advance in measuring soil moisture from satellites. However, its large spatia…The soil moisture active/passive (SMAP) mission represents a significant advance in measuring soil moisture from satellites. However, its large spatial-temporal data gaps limit the use of its values in near-real-time (NRT) applications. Considering this, the study uses NRT operational metadata (precipitation and skin temperature), together with some surface parameterization information, to feed into a random forest model to retrieve the missing values of the SMAP L3 soil moisture product. This practice was tested in filling the missing points for both SMAP descending (6:00 AM) and ascending orbits (6:00 PM) in a crop-dominated area from 2015 to 2019. The trained models with optimized hyper-parameters show the goodness of fit (R2 ≥ 0.86), and their resulting gap-filled estimates were compared against a range of competing products with in situ and triple collocation validation. This gap-filling scheme driven by low-latency data sources is first attempted to enhance NRT spatiotemporal support for SMAP L3 soil moisture.more
Monteiro, Maria José; Couto, Flavio T.; Bernardino, Mariana; Cardoso, Rita M.; Carvalho, David; Martins, João P. A.; Santos, João A.; Argain, José Luís; Salgado, Rui
Earth system modelling is currently playing an increasing role in weather forecasting and understanding climate change, however, the operation, deploy…Earth system modelling is currently playing an increasing role in weather forecasting and understanding climate change, however, the operation, deployment and development of numerical Earth system models are extremely demanding in terms of computational resources and human effort. Merging synergies has become a natural process by which national meteorological services assess and contribute to the development of such systems. With the advent of joining synergies at the national level, the second edition of the workshop on Numerical Weather Prediction in Portugal was promoted by the Portuguese Institute for the Sea and Atmosphere, I.P. (IPMA), in cooperation with several Portuguese Universities. The event was hosted by the University of Évora, during the period of 11–12 of November 2021. It was dedicated to surface–atmosphere interactions and allowed the exchange of experiences between experts, students and newcomers. The workshop provided a refreshed overview of ongoing research and development topics in Portugal on surface–atmosphere interaction modelling and its applications and an opportunity to revisit some of the concepts associated with this area of atmospheric sciences. This article reports on the main aspects discussed and offers guidance on the many technical and scientific modelling platforms currently under study.more