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
A framework was established for remote sensing of sea ice albedo that integrates sea ice physics with high computational efficiency and that can be ap…A framework was established for remote sensing of sea ice albedo that integrates sea ice physics with high computational efficiency and that can be applied to optical sensors that measure appropriate radiance data. A scientific machine learning (SciML) approach was developed and trained on a large synthetic dataset (SD) constructed using a coupled atmosphere-surface radiative transfer model (RTM). The resulting RTM-SciML framework combines the RTM with a multi-layer artificial neural network SciML model. In contrast to the Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43 albedo product, this framework does not depend on observations from multiple days and can be applied to single angular observations obtained under clear-sky conditions. Compared to the existing melt pond detection (MPD)-based approach for albedo retrieval, the RTM-SciML framework has the advantage of being applicable to a wide variety of cryosphere surfaces, both heterogeneous and homogeneous. Excellent agreement was found between the RTM-SciML albedo retrieval results and measurements collected from airplane campaigns. Assessment against pyranometer data (N=4144) yields RMSE = 0.094 for the shortwave albedo retrieval, while evaluation against albedometer data (N=1225) yields RMSE = 0.069, 0.143, and 0.085 for the broadband albedo in the visible, near-infrared, and shortwave spectral ranges, respectively.more
An estimate of solar irradiation potential over large regions requires the knowledge of the long-term spatio-temporal distribution of the solar radiat…An estimate of solar irradiation potential over large regions requires the knowledge of the long-term spatio-temporal distribution of the solar radiation as well as the identification of the suitable surfaces where the photovoltaic (PV) installations can be built. These main components can be modelled in different ways and are thus affected by different sources of uncertainty. Thus, when estimating the exploitable potential over large regions, it is important to measure the accuracy of the entire process. In this work, we provide a generic method to estimate the solar irradiation potential of rooftops over large regions and an estimate of the corresponding uncertainties when calculating the long-term electricity generation of PV plants. This method uses satellite based solar radiation data covering a period of 22 years, with a temporal resolution of 30 min and a spatial resolution of 3.8–5.6 km. Suitable surfaces on rooftops are identified using Digital Surface Models combined with building footprints. This allows to determine the geometry of rooftops, such as slope, and orientation with a spatial resolution of 0.5 m. Finally, we calculated the electricity generation based on models which take into account all characteristics of PV system components. In order to estimate the accuracy of the model for electricity production, we compared the monthly generation of 500 PV plants in Switzerland consisting of different PV technologies (mono-crystalline, poly-crystalline and thin film) with the estimates. The validation results show a correlation coefficient (R2) of 0.9 and a median monthly relative error between 0.28% (August) and 28.08% (December). The monthly estimates are more accurate during summer time, while spatially and technology-wise no significant differences are found.more
The Ocean and Sea-Ice Satellite Application Facility (OSI-SAF) of the EUropean Organisation for the Exploitation of METeorological Satellites (EUMETSA…The Ocean and Sea-Ice Satellite Application Facility (OSI-SAF) of the EUropean Organisation for the Exploitation of METeorological Satellites (EUMETSAT) has performed a reprocessing of Sea Surface Temperature (SST) from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard Meteosat Second Generation (MSG) archive (2004–2012).
The retrieval method consists of a non-linear split-window algorithm and an algorithm correction relying on simulations of infrared brightness temperatures performed using atmospheric profiles of water vapour and temperature from a Numerical Weather Prediction model, and a radiative transfer model. The cloud mask used is the Climate SAF reprocessing of the MSG/SEVIRI archive which is consistent over the period considered. Atmospheric Saharan dust has a strong impact on the retrieved SST in the Atlantic and Mediterranean regions, they are taken into consideration through the computation of the Saharan Dust Index (SDI) which is then used to determine an empirical correction applied to SST.
The reprocessing has benefited from the experience of the OSI SAF team in operational near real time processing of MSG/SEVIRI data, and the methods have been improved to provide a higher quality SST. The MSG/SEVIRI SST reprocessing dataset consists of hourly level 3 composites of sub-skin temperature projected onto a regular 0.05° grid over the region delimited by 60N,60S and 60W,60E. It has been thoroughly validated against drifting buoys and moored buoys measurements.
Results of this validation have shown that the reprocessed data record is of significantly better quality than the OSI SAF operational processing (for instance the day-time robust standard deviation is 0.45 K for the operational processing and 0.35 K for the reprocessed dataset). The data record has been used to characterize the diurnal variability of SST over large temporal and spatial scales.more