Generation Geostationary Satellite Research Articles

Atmospheric Profile Retrieval Algorithm for Next Generation Geostationary Satellite of Korea and Its Application to the Advanced Himawari Imager

In preparation for the 2nd geostationary multi-purpose satellite of Korea with a 16-channel Advanced Meteorological Imager; an algorithm has been developed to retrieve clear-sky vertical profiles of temperature (T) and humidity (Q) based on a nonlinear optimal estimation method. The performance and characteristics of the algorithm have been evaluated using the measured data of the Advanced Himawari Imager (AHI) on board the Himawari-8 of Japan, launched in 2014. Constraints for the optimal estimation solution are provided by the forecasted T and Q profiles from a global numerical weather prediction model and their error covariance. Although the information contents for temperature is quite low due to the limited number of channels used in the retrieval; the study reveals that useful moisture information (2~3 degrees of freedom for signal) is provided from the three water vapor channels; contributing to the increase in the moisture retrieval accuracy upon the model forecast. The improvements are consistent throughout the tropospheric atmosphere with almost zero mean bias and 9% (relative humidity) of root mean square error between 100 and 1000 hPa when compared with the quality-controlled radiosonde data from 2016 August.

Himawari-8 Atmospheric Motion Vectors - Operational Generation and Assimilation

In October 2014 the Japanese Meteorological Agency (JMA) launched the new generation geostationary satellite Himawari-8. This satellite provides ten minute imagery in sixteen wavebands over the Asian and Australasian region. The imagery has been navigated, calibrated and subsequently used in the Bureau of Meteorology (BoM) to generate Atmospheric Motion Vectors (AMVs) over the full earth disk viewed from the satellite every ten minutes. Each vector has been error characterised and assigned an expected error. In preparation for the operational assimilation of the ten minute data, these high temporal and spatial resolution data were used with the BoM operational database to provide forecasts from the next generation operational forecast model ACCESS APS2 using 4D Var. Results from these tests indicate these locally generated Himawari-8 ten minute AMVs are of high density and quality and have the potential to improve numerical weather prediction (NWP) model initialisation and forecasts. The forecasts undertaken include cases associated with extreme weather. The results also provided the appropriate times, data selection and application methods for the effective use of these high temporal resolution data. As a result of these studies these wind data were approved for inclusion in the BoMs operational database and are used in operational forecasting.

Detection of gravity waves in Meteosat imagery by grating cell operators

ABSTRACTAn effort was made to objectively identify regions of parallel stripes in images recorded by the Spinning Enhanced Visible and InfraRed Imager instrument on board the Meteosat Second Generation geostationary satellites. Such a grating pattern can signal the presence of gravity waves which eventually may lead to atmospheric turbulence, a hazard to be dealt with in the daily routine work of aviation meteorologists. A pattern detection algorithm, based on Gabor filters plus subsequently applied grating cell operators, is implemented, adapted and tested. The method is shown to be capable of identifying grating patterns despite the unfavourable relation between the scale of the sought waves and the spatial resolution of the imagery. Some phenomena producing similar patterns (hence similar responses of the algorithm) have been identified: marine stratocumulus and regular arrangements of mountain ranges and valleys. If present in the image, they have the potential to considerably impact the analyses. The results for the 7.3 µm water vapour channel are particularly encouraging in this context as filtering the unwanted alarms is straightforward in this case.

Himawari-8 Atmospheric Motion Vectors - Operational Generation and Assimilation

In October 2014 the Japanese Meteorological Agency (JMA) launched the new generation geostationary satellite Himawari-8. This satellite provides ten minute imagery in sixteen wavebands over the Asian and Australasian region. The imagery has been navigated, calibrated and subsequently used in the Bureau of Meteorology (BoM) to generate Atmospheric Motion Vectors (AMVs) over the full earth disk viewed from the satellite every ten minutes. Each vector has been error characterised and assigned an expected error. In preparation for the operational assimilation of the ten minute data, these high temporal and spatial resolution data were used with the BoM operational database to provide forecasts from the next generation operational forecast model ACCESS APS2 using 4D Var. Results from these tests indicate these locally generated Himawari-8 ten minute AMVs are of high density and quality and have the potential to improve numerical weather prediction (NWP) model initialisation and forecasts. The forecasts undertaken include cases associated with extreme weather. The results also provided the appropriate times, data selection and application methods for the effective use of these high temporal resolution data. As a result of these studies these wind data were approved for inclusion in the BoMs operational database and are used in operational forecasting.

Geostationary satellite‐based 6.7 μm band best water vapor information layer analysis over the Tibetan Plateau

AbstractThe best water vapor information layer (BWIL) of the 6.7 μm water vapor absorption infrared (IR) band for the FengYun‐2E is investigated over the Tibetan Plateau with standard atmospheric profile and European Centre for Medium‐Range Weather Forecasts (ECMWF) operational model analysis data. The sensitivity tests show that surface characteristics over the Tibetan Plateau have a significant influence on the BWIL. To be specific, topographic elevation, colder skin temperature, and lower emissivity tend to lift the altitude of the BWIL, decrease its magnitude, and narrow the half‐width range. The results from statistical analysis indicate that the altitude of the BWIL reaches the highest in summer and the lowest in winter. Meanwhile, the altitude of the BWIL is highly correlated with the water vapor amount above 500 hPa over the Tibetan Plateau and above 300 hPa over the East China Plain, respectively. The diurnal variation in the BWIL is synchronous with the diurnal variation in the surface skin temperature. It can be concluded from the study that surface characteristics over high terrain in dry and cold atmospheres have more significant impacts on the BWIL. With multiple water vapor absorption IR bands, the imagers on board the new generation of geostationary satellites will provide crucial improvement in water vapor remote sensing over the current single water vapor band on board the FY‐2 series according to the analysis in this study.

A Dynamic Threshold Cloud Detecting Approach based on the Brightness Temperature from FY-2 VISSR Data

Abstract. The traditional statistical methods and radiation transfer theory methods for cloud detecting have a high adaptability just only in those areas with a uniform surface coverage and noncomplex terrain. Therefore, adapted to large spatial and temporal scales, in this work a cloud detection method is developed, seeking the main influencing factors of the change of Brightness Temperature(BT) of clear sky and their relationships, researching the change regularity and normal fluctuation range of BT on the basis of function fitting, setting the cloud detecting dynamic threshold depending on the cloud spectral characteristics, and making accuracy assessment in order to ensure higher adaptability and accuracy of this cloud detecting method. In this paper, a dynamic threshold algorithm is presented for cloud detection using daytime imagery from the VISSR sensor on board FY-2C/D/E, which is the first generation geostationary satellite. And the land surface/brightness temperature influence functions are analysis and established, including latitude, longitude, altitude, time, land cover. The theoretical temperature value of clear sky can be calculated through these influence functions. Then, the dynamic threshold cloud detection model is proposed based on the high temporal resolution of VISSR data. Meanwhile, the land surface emissivity is considered as the main factor to the change range of brightness temperature which determines the dynamic threshold for cloud detection. Finally, the dynamic threshold cloud detecting model is evaluated using FY-2C/D/E VISSR data covering China, and the Kappa of dynamic method is maximum, equalling 0.6195, which is much higher than the indexes for the reflectivity and BT fixed methods, equalling 0.4511 and 0.403, respectively. Consequently, the dynamic threshold cloud detecting method provides an important improvement because the spatial, temporal and geographic characteristics were considered into the model.

A Simple Remote Sensing Ground Receiving System for Interest Creation in Systems Engineering and Geophysics Research

The METEOSAT Second Generation (MSG3) geostationary satellite main payload is the optical imaging radiometer, the so-called Spinning Enhanced Visible and InfraRed Imager (SEVIRI). With its 12 spectral channels and its high temporal resolution, SEVIRI can offer improved geophysics thematic products measurements. So, the development of a system that can exploit the temporal behavior of the terrestrial disk observations is crucial for these near-real-time applications. Advanced Very High Resolution Radiometer (AVHRR) data from polar-orbiting NOAA19 satellite are acquired and processed here. A ground station is required to collect and follow the temporal, spectral, and space evolution of the treated topic of interest. The main objective of this research is to give the most important directives for developing a simple remote sensing ground station. The system consists of hardware and software parts and gives meaningful results using received daily data from either SEVIRI-MSG3 or AVHRR-NOAA19. Satellite multi-sensor database is created and enriched everyday. A realized autotracker capable of automatic tracking satellites is mounted in our ground station. Based on received images, some acquisition and MSGViewer software processing results such as brightness temperature, Albedo conversions from the raw data, and different red–green–blue combinations between visible and infrared windows are illustrated and discussed. The system can be easily applied for operational research uses.

Guidance, navigation, and control performance for the GOES-R spacecraft

The Geostationary Operational Environmental Satellite-R series (GOES-R) is the first of the next generation geostationary weather satellites. The series represents a dramatic increase in Earth observation capabilities, with 4 times the resolution, 5 times the observation rate, and 3 times the number of spectral bands. GOES-R also provides unprecedented availability, with less than 120 min per year of lost observation time. This paper presents the guidance navigation & control (GN&C) requirements necessary to realize the ambitious pointing, knowledge, and image navigation and registration (INR) objectives of GOES-R. Because the suite of instruments is sensitive to disturbances over a broad spectral range, a high-fidelity simulation of the vehicle has been created with modal content over 500 Hz to assess the pointing stability requirements. Simulation results are presented showing acceleration, shock response spectra, and line-of-sight (LOS) responses for various disturbances from 0 to 512 Hz. Simulation results demonstrate excellent performance relative to the pointing and pointing stability requirements, with LOS jitter for the isolated instrument platform of approximately 1 micro-rad. Attitude and attitude rate knowledge are provided directly to the instrument with an accuracy defined by the integrated rate error requirements. The data are used internally for motion compensation. The final piece of the INR performance is orbit knowledge, which GOES-R achieves with GPS navigation. Performance results are shown demonstrating compliance with the 50–75 m orbit position accuracy requirements. As presented in this paper, the GN&C performance supports the challenging mission objectives of GOES-R.

Improved space borne detection of volcanic ash for real-time monitoring using 3-Band method

For over 25years, thermal infrared data supplied by satellite-based sensors are used to detect and characterize volcanic ash clouds using a commonly accepted method: the 2-Band reverse absorption technique. This method is based on a two-channel difference model using the opposite extinction features of water-ice and ash particles at 11 and 12μm wavelengths. Although quite efficient with the supervision of a user, this method shows however some limitations for reliable automated detection of volcanic ash in a real-time fashion. Here we explore a method dedicated to the operational monitoring of volcanic ash that combines the 11–12μm brightness temperature difference (BTD11–12) with a second brightness temperature difference between channels 8.7μm and 11μm, (BTD8.7–11). We first achieve a detailed microphysics analysis of different atmospheric aerosols (volcanic ash, water/ice, sulfuric acid, mineral dust) using optical properties (e.g., extinction efficiency, single scattering albedo and asymmetry parameter) calculated by Mie theory, and showing that BTD8.7–11 can be particularly efficient to remove most of artifacts. Then, we tested this method for eight different eruptions between 2005 and 2011 from six different volcanoes (Mount Etna, Piton de la Fournaise, Karthala, Soufriere Hills, Eyjafjallajökull, and Grimsvötn) using data from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board Meteosat Second Generation (MSG) geostationary satellite. We show that between 95.6% and 99.9% of ash-contaminated pixels erroneously identified by the BTD11–12 method (i.e., artifacts) were detected and removed by the 3-Band method. For all eruptions, the 3-Band method shows a high and constant reliability having a false alarm rate in the range 0.002–0.08%, hence allowing operational implementation for automated detection in case of a volcanic crisis.

Diurnal temperature cycle as observed by thermal infrared and microwave radiometers

Land surface temperature (LST) is a key input to physically-based retrieval algorithms of hydrological states and fluxes, and global measurements of LST are provided by many satellite platforms. Passive microwave (MW) observations offer an alternative to thermal infrared (TIR) LST retrieval approaches. Although MW has a lower spatial resolution, its temporal record is more complete as it is more tolerant to clouds. Moreover, merging TIR and MW LST with independent random errors should result in enhanced LST products. Despite these benefits, MW-based LST retrievals are not widely adopted for land applications except as an input to soil moisture retrieval algorithms. This research aims to facilitate expanded use of MW-based LST by formulating a model to explain the systematic differences in comparison to TIR-LST, and quantifying random errors in each datastream. To that end, we compile a 6-year MW-LST dataset over the African continent, combining observations from seven inter-calibrated low orbiting satellites equipped with suitable microwave radiometers. We compare the diurnal timing and amplitude of this dataset to TIR-LST time-series produced by LSA-SAF from the Meteosat Second Generation geostationary satellite. A third independent data source, the skin temperature as modeled by the Modern Era Reanalysis for Research and Applications (MERRA) as produced at NASA's Global Modeling and Assimilation Office, is included in a triple collocation analysis to calculate the random error in the amplitude anomaly. Results suggest that the anomaly in diurnal LST amplitude over much of the African domain can be estimated from TIR and MW with similar levels of random error for clear sky days, except over dry sandy desert areas where the MW sensing depth extends too far below the surface. The temporal sampling advantage of MW, due to higher cloud tolerance in comparison with TIR retrievals, appears to be substantial but will need to be verified by follow-on studies. Overall the results of this study present a significant step forward in reconciling the systematic differences in LST, preparing the way for a global merger of MW and TIR LST time-series.

AERUS‐GEO: A newly available satellite‐derived aerosol optical depth product over Europe and Africa

AbstractThis article presents a new aerosol optical depth (AOD) product delivered in near real time by the ICARE Data and Services Center to the scientific community. The AERUS‐GEO (Aerosol and surface albEdo Retrieval Using a directional Splitting method‐application to GEOstationary data) product is derived from observations from the Meteosat Second Generation (MSG) geostationary satellite covering Europe, Africa, and part of Asia and South America. The retrieval method exploits the directional information contained in the series of 96 MSG observations per day of the Earth's disk to derive a daily averaged AOD. The performances of this product are similar to those of other widely used satellite‐derived AOD. This article illustrates the advantages of the spatial (3 km at best) and temporal (daily) resolution of the AERUS‐GEO product to monitor particular aerosol activity (e.g., volcanic eruptions) or to study given phenomena (e.g., the impact of topography on aerosol loading).

Quantitative Differences between Lightning and Nonlightning Convective Rainfall Events as Observed with Polarimetric Radar and MSG Satellite Data

Abstract To increase understanding of the relationships between lightning and nonlightning convective storms, lightning observations from the National Aeronautics and Space Administration (NASA) African Monsoon Multidisciplinary Analyses (NAMMA) campaign were analyzed with Meteosat Second Generation (MSG) geostationary satellite and S-band NASA Polarimetric Doppler Weather Radar (NPOL) data. The study’s goal was to analyze the time evolution of infrared satellite fields and ground-based polarimetric radar during NAMMA to quantify relationships between satellite and radar observations for lightning and nonlightning convective clouds over equatorial Africa. Using NPOL data, very low-frequency arrival time difference lightning data, and MSG Spinning Enhanced Visible and Infrared Imager observations, the physical attributes of growing cumulus clouds, including ice mass production, updraft strength, cloud depth, and cloud-top glaciation were examined. It was found that, on average, the lightning storms had stronger updrafts (seen in the satellite and radar fields), which lead to the formation of deeper clouds (seen in the satellite and radar fields) and subsequently much more ice in the mixed-phase region (as confirmed in radar observations), as well as much more nonprecipitating ice in the top 1 km of the cloud (as quantified in both satellite and radar fields) than the nonlightning storms. Computed radar-derived ice masses in cumulus clouds verifies the traditional MSG indicators of cloud-top glaciation, while NPOL verifies internal structures (i.e., large amounts of graupel) where satellite and radar show strong updrafts.

Evapotranspiration monitoring with Meteosat Second Generation satellites: improvement opportunities from moderate spatial resolution satellites for vegetation

The Satellite Application Facility on Land Surface Analysis proposes a land evapotranspiration (ET) product, generated in near-real time. It is produced by an energy balance model forced by radiation components derived from data of the Spinning Enhanced Visible and Infrared Imager aboard Meteosat Second Generation geostationary satellites, at a spatial resolution of approximately 3 km at the equator and covering Europe, Africa, and South America. In this article, we assess the improvement opportunities from moderate spatial resolution satellites for ET monitoring at the Meteosat Second Generation satellite scale. Four variables, namely the land cover, the leaf area index (LAI), the surface albedo, and the open water fraction, derived from moderate-resolution satellites for vegetation monitoring are considered at two spatial resolutions, 1 km and 330 m, corresponding to the imagery provided by Satellite Pour l’Observation de la Terre (SPOT)-VEGETATION and future Project for On-Board Autonomy – Vegetation (PROBA-V) space-borne sensors. The variables are incorporated into the ET model, replacing or complementing input derived from the sensor aboard the geostationary satellite, and their relative effect on the model output is analysed. The investigated processes at small scales unresolved by the geostationary satellite are better taken into account in the final ET estimates, especially over heterogeneous and transition zones. Variables derived from sensors at 250–300 m are shown to have a noticeable effect on the ET estimates compared to the 1 km resolution, demonstrating the interest of PROBA-V 330 m-derived variables for the monitoring of ET at Meteosat Second Generation resolution.

Assessment of the EUMETSAT LSA-SAF evapotranspiration product for drought monitoring in Europe

Evapotranspiration is a key parameter for water stress assessment as it is directly related to the moisture status of the soil-vegetation system and describes the moisture transfer from the surface to the atmosphere. With the launch of the Meteosat Second Generation geostationary satellites and the setup of the Satellite Application Facilities, it became possible to operationally produce evapotranspiration data with high spatial and temporal evolution over the entire continents of Europe and Africa. In the frame of this study we present an evaluation of the potential of the evapotranspiration (ET) product from the EUMETSAT Satellite Application Facility on Land Surface Analysis (LSA-SAF) for drought assessment and monitoring in Europe.To assess the potential of this product, the LSA-SAF ET was used as input for the ratio of ET to reference evapotranspiration (ET0), the latter estimated from the ECMWF interim reanalysis. In the analysis two case studies were considered corresponding to the drought episodes of spring/summer 2007 and 2011. For these case studies, the ratio ET/ET0 was compared with meteorological drought indices (SPI, SPEI and Sc-PDSI for 2007 and SPI for 2011) as well as with the anomalies of the fraction of absorbed photosynthetic active radiation (fAPAR) derived from remote sensing data. The meteorological and remote sensing indicators were taken from the European Drought Observatory (EDO) and the CARPATCLIM climatological atlas.Results show the potential of ET/ET0 to characterize soil moisture variability, and to give additional information to fAPAR and to precipitation distribution for drought assessment. The main limitations of the proposed ratio for drought characterization are discussed, including options to overcome them. These options include the use of filters to discriminate areas with a low percentage vegetation cover or areas that are not in their growing period and the use of evapotranspiration without water restriction (ETwwr), obtained as output of the LSA-SAF model instead of ET0. The ET/ETwwr ratio was tested by comparing its accumulated values per growing period with the winter wheat yield values per country published by Eurostat. The results point to the potential of using the remote sensing based LSA-SAF evapotranspiration and the ET/ETwwr ratio for vegetation monitoring at large scale, especially in areas where data is generally lacking.

Volcanic hot spot detection from optical multispectral remote sensing data using artificial neural networks

SUMMARY This paper describes an application of artificial neural networks for the recognition of volcanic lava flow hot spots using remote sensing data. Satellite remote sensing is a very effective and safe way to monitor volcanic eruptions in order to safeguard the environment and the people affected by such natural hazards. Neural networks are an effective and well-established technique for the classification of satellite images. In addition, once well trained, they prove to be very fast in the application stage. In our study a back propagation neural network was used for the recognition of thermal anomalies affecting hot lava pixels. The network was trained using the three thermal channels of the Advanced Very High Resolution Radiometer (AVHRR) sensor as inputs and the corresponding values of heat flux, estimated using a two thermal component model, as reference outputs. As a case study the volcano Etna (Eastern Sicily, Italy) was chosen, and in particular the effusive eruption which took place during the month of 2006 July. The neural network was trained with a time-series of 15 images (12 nighttime images and 3 daytime images) and validated on three independent data sets of AVHRR images of the same eruption and on two relative to an eruption occurred the following month. While for both nighttime and daytime validation images the neural network identified the image pixels affected by hot lava with a 100 percent success rate, for the daytime images also adjacentpixelswereincluded,apparentlynotinterestedbylavaflow.Despitetheseperformance differences under different illumination conditions, the proposed method can be considered effective both in terms of classification accuracy and generalization capability. In particular our approach proved to be robust in the rejection of false positives, often corresponding to noisy or cloudy pixels, whose presence in multispectral images can often undermine the performance of traditional classification algorithms. Future work shall address application of the proposed method to data acquired with a high temporal resolution, such as those provided by the spinning enhanced visible and infrared imager sensor on board the Meteosat second generation geostationary satellite.

Comparison of diurnal warming estimates from unpumped Argo data and SEVIRI satellite observations

Estimates of diurnal warming at the ocean surface from modified Argo floats providing unpumped measurements of temperature up to the surface are compared against collocated satellite-derived values from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) flying on the METEOSAT-9 Second Generation (MSG) geostationary satellite. The amplitude of diurnal warming is computed from the difference between subskin and foundation temperature estimates derived independently from the Argo and SEVIRI data. The results demonstrate remarkable consistency between the observations, lending support for both products and the associated methodologies, particularly for the estimation of the foundation temperature. Individual subskin values agree to within an absolute mean difference of ≤0.1K and standard deviations of the differences are <0.4K. Statistics for comparison of the foundation temperatures are similar. Differences between the corresponding derived estimates of diurnal warming have negligible bias and standard deviations <0.25K. The strong agreement of the diurnal warming estimates exists even when excluding nearly isothermal profiles, suggesting the differences are robust to small spatial offsets and point-to-pixel differences. The results particularly support the ability of the modified Argo floats to provide reliable, and highly valuable, measurements of the near-surface temperature, helping to argue for more modified floats. Moreover, the results suggest that the unpumped Argo data has the potential to provide an independent estimate of the foundation temperature for validation of SST analyses. The method for estimating the foundation temperature from SEVIRI represents a good compromise between data coverage and influences of cloud contamination and nighttime cooling.

Phenology Estimation From Meteosat Second Generation Data

Many studies have focused on land surface phenology, for example as a means to characterize both water and carbon cycles for climate model inputs. However, the Spinning Enhanced Visible Infra-Red Imager (SEVIRI) sensor onboard Meteosat Second Generation (MSG) geostationary satellite has never been used for this goal. Here, five years of MSG-SEVIRI data have been processed to retrieve Normalized Difference Vegetation Index (NDVI) daily time series. Due to existing gaps as well as atmospheric and cloud contamination in the time series, an algorithm based on the iterative Interpolation for Data Reconstruction (IDR) has been developed and applied to SEVIRI NDVI time series, from which phenological parameters have been retrieved. The modified IDR (M-IDR) algorithm shows results of a similar quality to the original method, while dealing more efficiently with increased temporal resolution. The retrieved phenological phases were then analyzed and compared with an independent MODIS (Moderate resolution Imaging Spectrometer) dataset. Comparison of SEVIRI and MODIS-derived phenology with a pan-European ground phenology record shows a high accuracy of the SEVIRI-retrieved green-up and brown-down dates (within days) for most of the selected European validation sites, while differences with MODIS product are higher although this can be explained by differences in methodology. This confirms the potential of MSG data for phenological studies, with the advantage of a quicker availability of the data.

An Observing System Simulation Experiment for the Impact of MTG Candidate Infrared Sounding Mission on Regional Forecasts: System Development and Preliminary Results

An Observing System Simulation Experiment (OSSE) was designed and developed to assess the potential benefit of the Infrared Sounding on the Meteosat Third Generation (MTG-IRS) geostationary meteorological satellite system to regional forecasts. In the proposed OSSE framework, two different models, namely, the MM5 and WRF models, were used in a nature run and data assimilation experiments, respectively, to reduce the identical twin problem. The 5-day nature run, which included three convective storms that occurred during the period from 11 to 16 June 2002 over US Great Plains, was generated using MM5 with a 4 km. The simulated “conventional” observations and MTG-IRS retrieved temperature and humidity profiles, produced from the nature run, were then assimilated into the WRF model. Calibration experiments showed that assimilating real or simulated “conventional” observations yielded similar error statistics in analyses and forecasts, indicating that the developed OSSE system worked well. On average, the MTG-IRS retrieved profiles had positive impact on the analyses and forecasts. The analyses reduced the errors not only in the temperature and the humidity fields but in the horizontal wind fields as well. The forecast skills of these variables were improved up to 12 hours. The 18 h precipitation forecast accuracy was also increased.

Dust Detection and Optical Depth Retrieval Using MSG‑SEVIRI Data

Thanks to its observational frequency of 15 min, the Meteosat Second Generation (MSG) geostationary satellite offers a great potential to monitor dust storms. To explore this potential, an algorithm for the detection and the retrieval of dust aerosol optical properties has been tested. This is a multispectral algorithm based on visible and infrared data which has been applied to 15 case studies selected between 2007 and 2011. The algorithm has been validated in the latitude–longitude box between 30 and 50 degrees north, and −10 and 20 degrees east, respectively. Hereafter we present the obtained results that have been validated against Aerosol Robotic Network (AERONET) ground-based measurements and compared with the retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA’s Terra and Aqua satellites. The dust aerosol optical depth variations observed at the AERONET sites are well reproduced, showing good correlation of about 0.77, and a root mean square difference within 0.08, and the spatial patterns retrieved by using the algorithm developed are in agreement with those observed by MODIS.

Impact Assessment of Daily Satellite-Derived Surface Albedo in a Limited-Area NWP Model

AbstractThis study examines the impact of daily satellite-derived albedos on short-range forecasts in a limited-area numerical weather prediction (NWP) model over Europe. Contrary to previous studies in which satellite products were used to derive monthly “climatologies,” a daily surface (snow free) albedo is analyzed by a Kalman filter. The filter combines optimally a satellite product derived from the Meteosat Second Generation geostationary satellite [and produced by the Land Surface Analyses–Satellite Application Facility of the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)], an albedo climatology, and a priori information given by “persistence.” The surface albedo analyzed for a given day is used as boundary conditions of the NWP model to run forecasts starting the following day. Results from short-range forecasts over a 1-yr period reveal the capacity of satellite information to reduce model biases and RMSE in screen-level temperature (during daytime and intermediate seasons). The impact on forecast scores is larger when considering the analyzed surface albedo rather than another climatologically based albedo product. From comparisons with measurements from three flux-tower stations over mostly homogeneous French forests, it is seen that the model biases in surface net radiation are significantly reduced. An impact on the whole planetary boundary layer, particularly in summer, results from the use of an observed surface albedo. An unexpected behavior produced in summer by the satellite-derived albedo on surface temperature is also explained. The forecast runs presented here, performed in dynamical adaptation mode, will be complemented later on by data assimilation experiments over typically monthly periods.