Abstract. A method for detailed evaluation of a new satellite-derived global 28 yr cloud and radiation climatology (Climate Monitoring SAF Clouds, Alb…Abstract. A method for detailed evaluation of a new satellite-derived global 28 yr cloud and radiation climatology (Climate Monitoring SAF Clouds, Albedo and Radiation from AVHRR data, named CLARA-A1) from polar-orbiting NOAA and Metop satellites is presented. The method combines 1 km and 5 km resolution cloud datasets from the CALIPSO-CALIOP (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation – Cloud-Aerosol Lidar with Orthogonal Polarization) cloud lidar for estimating cloud detection limitations and the accuracy of cloud top height estimations. Cloud detection is shown to work efficiently for clouds with optical thicknesses above 0.30 except for at twilight conditions when this value increases to 0.45. Some misclassifications of cloud-free surfaces during daytime were revealed for semi-arid land areas in the sub-tropical and tropical regions leading to up to 20% overestimated cloud amounts. In addition, a substantial fraction (at least 20–30%) of all clouds remains undetected in the polar regions during the polar winter season due to the lack of or an inverted temperature contrast between Earth surfaces and clouds. Subsequent cloud top height evaluation took into account the derived information about the cloud detection limits. It was shown that this has fundamental importance for the achieved results. An overall bias of −274 m was achieved compared to a bias of −2762 m when no measures were taken to compensate for cloud detection limitations. Despite this improvement it was concluded that high-level clouds still suffer from substantial height underestimations, while the opposite is true for low-level (boundary layer) clouds. The validation method and the specifically collected satellite dataset with optimal matching in time and space are suggested for a wider use in the future for evaluation of other cloud retrieval methods based on passive satellite imagery.more
The long-term comparison between simulated and observed spectrally resolved outgoing longwave radiation (OLR) can represent a stringent test for the d…The long-term comparison between simulated and observed spectrally resolved outgoing longwave radiation (OLR) can represent a stringent test for the direct verification and improvement of general circulation models (GCMs), which are regularly tuned by adjusting parameters related to subgrid processes not explicitly represented in the model to constrain the integrated OLR energy fluxes to observed values. However, a good agreement between simulated and observed integrated OLR fluxes may be obtained from the cancellation of opposite-in-sign systematic errors localized in specific spectral ranges.Since the mid-2000s, stable hyperspectral observations of the mid-infrared region (667 to 2750 cm(-1)) of the Earth emission spectrum have been provided by different sensors (e.g. AIRS, IASI and CrIS). Furthermore, the FORUM (Far-infrared Outgoing Radiation Understanding and Monitoring) mission, selected to be the ninth ESA Earth Explorer, will measure, starting from 2027, the terrestrial radiation emitted to space at the top of the atmosphere (TOA) from 100 to 1600 cm(-1), filling the observational gap in the far-infrared (FIR) region, from 100 to 667 cm(-1).In this work, in anticipation of FORUM measurements, we compare Infrared Atmospheric Sounding Interferometer (IASI) Metop-A observations to radiances simulated on the basis of the atmospheric fields predicted by the EC-Earth Global Climate Model (version 3.3.3) in clear-sky conditions. To simulate spectra based on the atmospheric and surface state provided by the climate model, the radiative transfer model sigma-IASI has been integrated in the Cloud Feedback Model Intercomparison Project (COSP) package. Therefore, online simulations, provided by the EC-Earth model equipped with the new COSP-sigma-IASI module, have been performed in clear-sky conditions with prescribed sea surface temperature and sea ice concentration, every 6 h, over a time frame consistent with the availability of IASI data.Systematic comparisons between observed and simulated brightness temperature (BT) have been performed in 10 cm(-1) spectral intervals, on a global scale over the ocean, with a specific focus on the latitudinal belt between 30 degrees S and 30 degrees N.The analysis has shown a warm BT bias of about 3.5 K in the core of the CO2 absorption band and a cold BT bias of approximately 1 K in the wing of the CO2 band, due to a positive temperature bias in the stratosphere and a negative temperature bias in the middle troposphere of the climate model, respectively. Finally, considering a warm BT bias in the rotational-vibrational water vapour band, we have highlighted a dry bias of the water vapour concentration in the upper troposphere of the model.more
Opportunistic constant target matching is a new method for satellite intercalibration. It solves a long-standing issue with the traditional simultaneo…Opportunistic constant target matching is a new method for satellite intercalibration. It solves a long-standing issue with the traditional simultaneous nadir overpass (SNO) method, namely, that it typically provides only data points with cold brightness temperatures for humidity sounding instruments on sun-synchronous satellites. In the new method, a geostationary infrared sensor (SEVIRI) is used to select constant target matches for two different microwave sensors (MHS on NOAA 18 and Metop A). We discuss the main assumptions and limitations of the method and explore its statistical properties with a simple Monte Carlo simulation. The method was tested in a simple case study with real observations for this combination of satellites for MHS Channel 3 at 183 ± 1 GHz, the upper tropospheric humidity channel. For the studied 3-month test period, real observations are found to behave consistently with the simulations, increasing our confidence that the method can be a valuable tool for intercalibration efforts. For the selected case study, the new method confirms that the bias between NOAA 18 and Metop A MHS Channel 3 is very small, with absolute value below 0.05 K.more
Cloud ice particle effective radius in atmospheric models is usually parametrized. A widely-used parametrization comprises a strong dependence on the …Cloud ice particle effective radius in atmospheric models is usually parametrized. A widely-used parametrization comprises a strong dependence on the temperature. Utilizing available satellite-based estimates of both cloud ice particle effective radius and cloud-top temperature we evaluate if a similar temperature-dependence exists in these observations. We find that for very low cloud-top temperatures the modeled cloud ice particle effective radius generally agrees on average with satellite observations. For high sub-zero temperatures however, the modeled cloud ice particle effective radius becomes very large, which is not seen in the satellite observations. We conclude that the investigated parametrization for the cloud ice particle effective radius, and parametrizations with a similar temperature dependence, likely produce systematic biases at the cloud top. Supporting previous studies, our findings suggest that the vertical structure of clouds should be taken into account as factor in potential future updates of the parametrizations for cloud ice particle effective radius.
Plain Language Summary Atmospheric models are often used to diagnose and predict the atmospheric state including clouds. One very important property of clouds that consist of ice particles is the cloud ice particle effective radius. This ice effective radius is based on assumptions about the size and shapes of the ice particles in clouds, and thus parametrized, and is one of the important variables needed for calculating the effect of clouds on electromagnetic radiation, in particular on the solar radiation that enters the Earth's atmosphere. In our study we found that the parametrized ice effective radius agrees well on average and global scale with the ice effective radius inferred from satellite observations for cold clouds. However, we also found that for warmer ice clouds the parametrized ice effective radius is much higher than in satellite observations. Our study suggests that parametrizations of the ice effective radius used in atmospheric models show potential for improvements.more
Solar resource data derived from satellite imagery are widely available nowadays, either as an open-source or paid database. This article is intended …Solar resource data derived from satellite imagery are widely available nowadays, either as an open-source or paid database. This article is intended to assess open-source databases, which cover the region of Indonesia. Here, four known solar resource databases, which spatially cover the Indonesian archipelago, have been used, namely, Prediction of Worldwide Energy Resource (POWER), Surface Solar Radiation–Heliosat-East (SARAH-E), CM SAF Cloud, Albedo, Radiation edition 2 (CLARA-A2), and SolarGIS. In addition, a minor portion of the Meteonorm database by Meteotest, around five sample points across Indonesia, has been assessed in terms of coherency to the four mentioned databases. Correlation coefficient and relative bias of the multiyear monthly mean annual cycle global horizontal irradiation (GHI) between pairs of databases are inspected. Three out of four databases are then validated through the available irradiation ground measurement data provided by the World Radiation Data Centre (WRDC). The correlation between each pair varies mostly between 0.7 and 1, which shows that the four databases to a certain extent agree on how the intermonthly variation would behave throughout the year. On the other hand, the validation result reveals that the three databases, i.e., POWER, CLARA-A2, and SARAH-E, are suffering from positive bias error ranging from 3% to 7%. Despite that fact, the correlation between measured and estimated values is still acceptable with SARAH-E showing the best performance among the three. Careful selections and adjustment enable the possibility of these databases to be utilized as a tool for depicting interannual and intermonthly variations of solar irradiation throughout the Indonesian archipelago.more
Four parameterizations, distinguishing between land and ocean, have been developed to simulate global distributions of thundercloud streamer corona di…Four parameterizations, distinguishing between land and ocean, have been developed to simulate global distributions of thundercloud streamer corona discharges (also known as Blue LUminous Events or BLUEs) mainly producing bluish optical emissions associated with the second positive system of N2 accompanied by no (or hardly detectable) 777.4 nm light emission. BLUEs occur globally about 12 times less frequently (Soler et al., 2022) than lightning flashes. The four schemes are based on non-linear functions of the cloud-top height (CTH), the product of the convective available potential energy (CAPE) and total precipitation (TP), the product of CAPE and specific cloud liquid water content (CLWC), and the product of CAPE and specific cloud snow water content (CSWC). Considering that thunderstorms occur on hourly timescales, these parameterizations have been tested using hourly ERA5 data (except for CTH, not available in ERA5) for the meteorological variables considered, finding that the proposed BLUE schemes work fine and are consistent with observations by the Atmosphere–Space Interactions Monitor (ASIM). Moreover, the parameterizations have been implemented in a global chemistry–climate model that generates annual and seasonal global distributions for present-day and end of 21st century climate scenarios. Present-day predictions are in reasonable agreement with recent observations by the ASIM. Predictions for the end of the 21st century suggest BLUE occurrence rates that range between 13 % higher (∼ 3 % K−1) and 52 % higher (∼ 13 % K−1) than present-day average occurrences of BLUEs.more
We present the largest sensitivity study to date for cloud cover using the Weather Forecasting and Research model (WRF V3.7.1) on the European domain.…We present the largest sensitivity study to date for cloud cover using the Weather Forecasting and Research model (WRF V3.7.1) on the European domain. The experiments utilize the meteorological part of a large-ensemble framework, ESIAS-met (Ensemble for Stochastic Integration of Atmospheric Simulations). This work demonstrates the capability and performance of ESIAS for large-ensemble simulations and sensitivity analysis. The study takes an iterative approach by first comparing over 1000 combinations of microphysics, cumulus parameterization, planetary boundary layer (PBL) physics, surface layer physics, radiation scheme, and land surface models on six test cases. We then perform more detailed studies on the long-term and 32-member ensemble forecasting performance of select combinations. The results are compared to CM SAF (Climate Monitoring Satellite Application Facility) satellite images from EUMETSAT (European Organisation for the Exploitation of Meteorological Satellites). The results indicate a high sensitivity of clouds to the chosen physics configuration. The combination of Goddard, WRF single moments 6 (WSM6), or CAM5.1 microphysics with MYNN3 (Mellor-Yamada Nakanishi Niino level 3) or ACM2 (Asymmetrical Convective Model version 2) PBL performed best for simulating cloud cover in Europe. For ensemble-based probabilistic simulations, the combinations of WSM6 and SBU-YLin (Stony Brook University Y. Lin) microphysics with MYNN2 and MYNN3 performed best.more
The SM2RAIN algorithm developed a simple analytical relationship by inverting the soil-water equation to estimate rainfall through the knowledge of so…The SM2RAIN algorithm developed a simple analytical relationship by inverting the soil-water equation to estimate rainfall through the knowledge of soil moisture. The recently developed SM2RAIN-NWF algorithm offers an improvement in estimating rainfall by integrating the SM2RAIN algorithm and the net water flux (NWF) model. The Advanced Scatterometer (ASCAT) soil moisture products were used to estimate rainfall and evaluate the reliability of the SM2RAIN-NWF algorithm compared to the SM2RAIN on a national scale. Besides, the impact of Land cover-Soil texture-Climate (LSC) characteristics and the intensity of rainfall (four classes of intensity) on the performance of algorithms were discussed. Five performance metrics, including Correlation Coefficient (R), Kling–Gupta (KGE), Root Mean Square Error (RMSE), False Alarm Ration (FAR), and Probability of Detection (POD) were used to validate the estimated cumulative 5-, 14-, and 30-day rainfall. Furthermore, the effect of evapotranspiration (ET) and drainage terms were investigated in the performance of rainfall estimation through the SM2RAIN-NWF algorithm for the first time on a national scale. Results showed the rainfall estimations through the SM2RAIN-NWF algorithm improved approximately up to 7.5% in each accumulation (e.g. rainfall aggregation intervals (AGGR) 5 to 14 and 14 to 30) based on R and KGE indices. In addition, the SM2RAIN-NWF improved rainfall estimations up to 50% based on the KGE index in the southern half of Iran (arid and semi-arid climate) compared to the SM2RAIN estimates. The comprehensive evaluation and uncertainty analysis of rainfall estimations under the supervised classification of 11 LSC and 4 rainfall classes also showed the calibration of the SM2RAIN-NWF was highly affected by environmental and climatic circumstances. Uncertainty analysis showed the SM2RAIN-NWF algorithm can estimate rainfall more consistently in the five LSC classes namely 1) barren-clay loam-arid-desert, 2) barren-loam-arid-steppe, 3) barren-clay loam-arid-steppe, 4) urban-clay loam-arid-desert, and 5) urban-loam-arid-steppe. Similarly, estimating rainfall in the region with precipitation under 267 mm/year can be retrieved more reliably through the SM2RAIN-NWF algorithm. Results obtained from the ET analysis revealed an insignificant (more