Abstract
Three cloud-phase determination algorithms from passive satellite imagers are explored to assess their suitability for climate mo…Abstract
Three cloud-phase determination algorithms from passive satellite imagers are explored to assess their suitability for climate monitoring purposes in midlatitude coastal climate zones. The algorithms are the Moderate Resolution Imaging Spectroradiometer (MODIS)-like thermal infrared cloud-phase method, the Satellite Application Facility on Climate Monitoring (CM-SAF) method, and an International Satellite Cloud Climatology Project (ISCCP)-like method. Using one year (May 2004–April 2005) of data from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on the first Meteosat Second Generation satellite (Meteosat-8), retrievals of the methods are compared with collocated and synchronized ground-based cloud-phase retrievals obtained from cloud radar and lidar observations at Cabauw, Netherlands. Three aspects of the satellite retrievals are evaluated: 1) instantaneous cloud-phase retrievals, 2) monthly-averaged water and ice cloud occurrence frequency, and 3) diurnal cycle of cloud phase for May–August 2004. For the instantaneous cases, all methods have a very small bias for thick water and ice cloud retrievals (∼5%). The ISCCP-like method has a larger bias for pure water clouds (∼10%), which is likely due to the 260-K threshold leading to misdetection of water clouds existing at lower temperatures. For the monthly-averaged water and ice cloud occurrence, the CM-SAF method is best capable of reproducing the annual cycle, mainly for the water cloud occurrence frequency, for which an almost constant positive bias of ∼8% was found. The ISCCP- and MODIS-like methods have more problems in detecting the annual cycle, especially during the winter months. The difference in annual cycle detection among the three methods is most probably related to the use of visible/near-infrared reflectances that enable a more direct observation of cloud phase. The diurnal cycle in cloud phase is reproduced well by all methods. The MODIS-like method reproduces the diurnal cycle best, with correlations of 0.89 and 0.86 for water and ice cloud occurrence frequency, respectively.more
Downward shortwave radiation (DSR) is a key component of the surface energy budget, influencing atmospheric circulation and climate change. DSR produc…Downward shortwave radiation (DSR) is a key component of the surface energy budget, influencing atmospheric circulation and climate change. DSR products derived from remote sensing observations or generated from reanalysis systems are commonly used as inputs for ecohydrological and climate models. The Loess Plateau is severely affected by soil erosion and has experienced frequent extreme weather events in recent years. Therefore, an accurate DSR product is crucial for accurately simulating climate change and surface-atmosphere processes on the Loess Plateau. In this study, newly released satellite DSR products CLouds, Albedo and Radiation Edition 3 data (CLARA-A3) and Moderate Resolution Imaging Spectroradiometer land surface Downward Shortwave Radiation Version 6.1 data (MCD18A1 V6.1), along with the reanalysis product Land component of the Fifth Generation European Centre for Medium-Range Weather Forecasts Reanalysis (ERA5-Land), were evaluated over the Loess Plateau and its surrounding areas. Intraday, daily, monthly, and seasonal DSR were evaluated against ground measurements which were collected from five observation networks. CLARA-A3 outperformed MCD18A1 and ERA5-Land on both monthly and daily scales. The root-mean-square error for monthly (daily) DSR from CLARA-A3, ERA5-Land, and MCD18A1 were 19.31 (31.3) W/m2, 25.36 (39.74) W/m2, and 25.03 (46.14) W/m2, respectively. The study explored potential factors contributing to significant errors in DSR products. Results indicated that snow cover was one possible factor influencing the error in MCD18A1, and CLARA-A3 exhibited greater sensitivity to terrain influence compared to ERA5-Land and MCD18A1. The findings can be the reference for selecting DSR products over the Loess Plateau.more
Satellite-based precipitation products, with simultaneously high spatial and temporal resolutions, are mostly needed to assess climate change repercus…Satellite-based precipitation products, with simultaneously high spatial and temporal resolutions, are mostly needed to assess climate change repercussions. Previous research used datasets neglecting either good temporal or good spatial resolution, PERSIANN-CCSCDR, ERA5, and SM2RAIN-ASCAT are some of the projects aiming to remedy these limitations. This study's goal is to evaluate the accuracy of the PERSIANN-CCS-CDR, ERA5, and SM2RAIN-ASCAT at a monthly scale and their suitability for drought assessment in a Moroccan semiarid watershed. Several statistical indices were computed, the drought SPI was calculated using PERSIANN-CCS-CDR estimates, ERA5 products, and observed records as an input in the SPI formula using Gamma distribution to simulate drought from 1983 to 2017. The preliminary comparison and evaluation results of PERSIANN-CCS-CDR estimates and ERA5 datasets showed good CC on a basin scale for monthly precipitation, with a slight overestimation of the observed precipitation shown by the PBIAS. The NSE scored 0.41 for PERSIANN-CCS-CDR and 0.72 for ERA5. The results for SM2RAIN-ASCAT showed an overestimation of the observed precipitation data. At the basin scale, the SPI3 correlation coefficients between the PERSIANN-CCS-CDR monthly estimates and observed gauge rainfall data were greater than 0.67, and the RMSE was closer to 0, outperforming ERA5 in the SPI3 evaluation.more
Rapid warming of the Arctic has resulted in widespread sea ice loss. Sea ice radiative forcing (SIRF) is the instantaneous perturbation of Earth’s rad…Rapid warming of the Arctic has resulted in widespread sea ice loss. Sea ice radiative forcing (SIRF) is the instantaneous perturbation of Earth’s radiation at the top of the atmosphere (TOA) caused by sea ice. Previous studies focused only on the role of albedo on SIRF. Skin temperature is also closely related to sea ice changes and is one of the main factors in Arctic amplification. In this study, we estimated SIRF considering both surface albedo and skin temperature using radiative kernels. The annual average net-SIRF, which consists of the sum of albedo-SIRF and temperature-SIRF, was calculated as −54.57 ± 3.84 W/m2 for the period 1982–2015. In the net-SIRF calculation, albedo-SIRF and temperature-SIRF made similar contributions. However, the albedo-SIRF changed over the study period by 0.12 ± 0.07 W/m2 per year, while the temperature-SIRF changed by 0.22 ± 0.07 W/m2 per year. The SIRFs for each factor had different patterns depending on the season and region. In summer, rapid changes in the albedo-SIRF occurred in the Kara and Barents regions. In winter, only a temperature-SIRF was observed, and there was little difference between regions compared to the variations in albedo-SIRF. Based on the results of the study, it was concluded that the overall temperature-SIRF is changing more rapidly than the albedo-SIRF. This study indicates that skin temperatures may have a greater impact on the Arctic than albedo in terms of sea ice surface changes.more
Abstract. We here present results from an evaluation of the Radio Occultation Meteorology Satellite Application Facility (ROM SAF) gridded monthly mea…Abstract. We here present results from an evaluation of the Radio Occultation Meteorology Satellite Application Facility (ROM SAF) gridded monthly mean climate data record (CDR v1.0),
based on Global Positioning System (GPS) radio occultation (RO) data from the CHAMP (CHAllenging Minisatellite Payload), GRACE (Gravity Recovery and
Climate Experiment), COSMIC (Constellation Observing System for Meteorology, Ionosphere, and Climate),
and Metop satellite missions.
Systematic differences between RO missions, as well as differences of RO data relative to ERA-Interim
reanalysis data, are quantified. The methods used to generate gridded monthly mean data are described, and
the correction of monthly mean RO climatologies for sampling errors, which is essential for
combining data from RO missions with different sampling characteristics, is evaluated. We find good overall agreement between the ROM SAF gridded monthly mean CDR and the ERA-Interim reanalysis,
particularly in the 8–30 km height interval. Here, the differences largely reflect time-varying biases
in ERA-Interim, suggesting that the RO data record has a better long-term stability than
ERA-Interim. Above 30–40 km altitude, the differences are larger, particularly for the pre-COSMIC era. In the 8–30 km altitude region, the observational data record exhibits a high degree of
internal consistency between the RO satellite missions, allowing us to combine data into
multi-mission records.
For global mean bending angle, the consistency is better than 0.04 %, for refractivity it is better than 0.05 %, and for global mean
dry temperature the consistency is better than 0.15 K in this height interval. At altitudes
up to 40 km, these numbers increase
to 0.08 %, 0.11 %, and 0.50 K, respectively. The numbers can be up to a factor of 2 larger for certain latitude
bands compared to global means. Below about 8 km, the RO mission differences are larger, reducing the possibilities
to generate multi-mission data records.
We also find that the residual sampling errors are about one-third of the original and that they include a component
most likely related to diurnal or semi-diurnal cycles.more
Since a decade, convection-permitting regional climate models (CPRCM) have emerged showing promising results, especially in improving the simulation o…Since a decade, convection-permitting regional climate models (CPRCM) have emerged showing promising results, especially in improving the simulation of precipitation extremes. In this article, the CPRCM CNRM-AROME developed at the Centre National de Recherches Météorologiques (CNRM) since a few years is described and evaluated using a 2.5-km 19-year long hindcast simulation over a large northwestern European domain using different observations through an added-value analysis in which a comparison with its driving 12-km RCM CNRM-ALADIN is performed. The evaluation is challenging due to the lack of high-quality observations at both high temporal and spatial resolutions. Thus, a high spatio-temporal observed gridded precipitation dataset was built from the collection of seven national datasets that helped the identification of added value in CNRM-AROME. The evaluation is based on a series of standard climatic features that include long-term means and mean annual cycles of precipitation and near-surface temperature where CNRM-AROME shows little improvements compared to CNRM-ALADIN. Additional indicators such as the summer diurnal cycle and indices of extreme precipitation show, on the contrary, a more realistic behaviour of the CNRM-AROME model. Moreover, the analysis of snow cover shows a clear added-value in the CNRM-AROME simulation, principally due to the improved description of the orography with the CPRCM high resolution. Additional analyses include the evaluation of incoming shortwave radiation, and cloud cover using satellite estimates. Overall, despite some systematic biases, the evaluation indicates that CNRM-AROME is a suitable CPRCM that is superior in many aspects to the RCM CNRM-ALADIN.more
Accurate global horizontal irradiance (GHI) forecasting is critical for integrating solar energy into the power grid and operating solar power plants.…Accurate global horizontal irradiance (GHI) forecasting is critical for integrating solar energy into the power grid and operating solar power plants. The Weather Research and Forecasting model with its solar radiation extension (WRF-Solar) has been used to forecast solar irradiance in different regions around the world. However, the application of the WRF-Solar model to the prediction of GHI in West Africa, particularly Ghana, has not yet been investigated. The aim of this study is to evaluate the performance of the WRF-Solar model for predicting GHI in Ghana, focusing on three automatic weather stations (Akwatia, Kumasi and Kologo) for the year 2021. We used two one-way nested domains (D1 = 15 km and D2 = 3 km) to investigate the ability of the fully coupled WRF-Solar model to forecast GHI up to 72-hour ahead under different atmospheric conditions. The initial and lateral boundary conditions were taken from the ECMWF high-resolution operational forecasts. Our findings reveal that the WRF-Solar model performs better under clear skies than cloudy skies. Under clear skies, Kologo performed best in predicting 72-hour GHI, with a first day nRMSE of 9.62 %. However, forecasting GHI under cloudy skies at all three sites had significant uncertainties. Additionally, WRF-Solar model is able to reproduce the observed GHI diurnal cycle under high AOD conditions in most of the selected days. This study enhances the understanding of the WRF-Solar model’s capabilities and limitations for GHI forecasting in West Africa, particularly in Ghana. The findings provide valuable information for stakeholders involved in solar energy generation and grid integration towards optimized management in the region.more