Atmospheric Motion Vectors (AMVs) calculated by six different institutions (Brazil Center for Weather Prediction and Climate Studies/CPTEC/INPE, European Organization for the Exploitation of Meteorological Satellites/EUMETSAT, Japan Meteorological Agency/JMA, Korea Meteorological Administration/KMA, Unites States National Oceanic and Atmospheric Administration/NOAA, and the Satellite Application Facility on Support to Nowcasting and Very short range forecasting/NWCSAF) with JMA’s Himawari-8 satellite data and other common input data are here compared. The comparison is based on two different AMV input datasets, calculated with two different image triplets for 21 July 2016, and the use of a prescribed and a specific configuration. The main results of the study are summarized as follows: (1) the differences in the AMV datasets depend very much on the ‘AMV height assignment’ used and much less on the use of a prescribed or specific configuration; (2) the use of the ‘Common Quality Indicator (CQI)’ has a quantified skill in filtering collocated AMVs for an improved statistical agreement between centers; (3) Among the six AMV operational algorithms verified by this AMV Intercomparison, JMA AMV algorithm has the best overall performance considering all validation metrics, mainly due to its new height assignment method: ‘Optimal estimation method considering the observed infrared radiances, the vertical profile of the Numerical Weather Prediction wind, and the estimated brightness temperature using a radiative transfer model’.
Atmospheric Motion Vectors (AMVs) calculated by six different institutions (Brazil Center for Weather Prediction and Climate Studies/CPTEC/INPE, European Organization for the Exploitation of Meteorological Satellites/EUMETSAT, Japan Meteorological Agency/JMA, Korea Meteorological Administration/KMA, Unites States National Oceanic and Atmospheric Administration/NOAA and the Satellite Application Facility on Support to Nowcasting/NWCSAF) with JMA’s Himawari-8 satellite data and other common input data are here compared. The comparison is based on two different AMV input datasets, calculated with two different image triplets for 21 July 2016, and the use of a prescribed and a specific configuration. The main results of the study are summarized as follows: (1) the differences in the AMV datasets depend very much on the “AMV height assignment” used and much less on the use of a prescribed or specific configuration; (2) the use of the “Common Quality Indicator (CQI)” has a quantified skill in filtering collocated AMVs for an improved statistical agreement between centers; (3) JMA AMV algorithm has the best overall performance considering all validation metrics, most likely due to its height assignment: “optimal estimation using observed radiance and NWP wind vertical profile”.
This paper summarizes the successful use of Geostationary Operational Environmental Satellite-10 (GOES-10) and -12 (GOES-12), mainly beyond their retirement as operational satellites in the United States, in support of meteorological activities in South America (SA). These satellites were maneuvered by the National Oceanic and Atmospheric Administration (NOAA) to approximately 60°W, enabling other countries in Central and South America to benefit from their ongoing measurements. The extended usefulness of GOES-10 and -12 was only possible as a result of a new image geolocalization system developed by NOAA for correcting image distortions and evaluated in collaboration with the Brazilian National Institute for Space Research. The extension allowed GOES-10 and -12 to monitor SA for an additional 7 years proving the efficiency of this navigation capability implemented for the first time in the GOES series well beyond the expected satellites’ lifetime. Such successful capability is incorporated in the new-generation GOES-R series. This practical and technological experience shows the importance of communication between scientists from the United States and SA for advancing Earth’s monitoring system through the development of novel software and derived products. For SA in particular, GOES-10 and -12 were employed operationally to monitor dry spells, relevant for agriculture and forest fire management and to nowcast severe weather for flash flood warnings. Additionally, GOES-12 detected the first registered tropical hurricane over the Brazilian coast. This paper describes some of the technical and operational challenges faced in extending the GOES-10 and -12 missions to provide coverage over South America and emphasizes the usefulness of their ongoing measurements benefiting Brazilian environmental monitoring.
Advances in computer power have made it possible to increase the spatial resolution of regional numerical models to a scale encompassing larger convective elements of less than 5 km in size. One goal of high resolution is to begin to resolve convective processes, and therefore it is necessary to evaluate the realism of convective clouds resolved explicitly at this resolution. This paper presents a method that is based on satellite comparisons to examine the simulation of continental tropical convection over Africa, in a high-resolution integration of the Met Office Unified Model (UK UM), developed under the Cascade project. The spatial resolution of these simulations is 1.5 km, the temporal resolution is 15 min, and the convection is resolved explicitly. The Spinning Enhanced Visible and Infrared Imager (SEVIRI) radiometer measurements were simulated by the Radiative Transfer for the Television and Infrared Observation Satellite (TIROS) Operational Vertical Sounder (RTTOV) model, and then a comparison between the simulations and real SEVIRI measurements was performed. The analysis using the presented method shows that the UK UM can represent tropical convection dynamics realistically. However, an error has been found in the high-level humidity distribution, which is characterized by strong humidity gradients. A key point of this paper is to present a method for establishing the credibility of a convection-permitting model by direct comparison with satellite data.
This research explores the benefits of radar data assimilation for short-range weather forecast in Southeastern Brazil using the Weather Research and Forecasting (WRF) model’s three-dimensional variational data assimilation (3DVAR) system. Different data assimilation options are explored, including the cycling frequency, the number of outer loops and the use of null-echo assimilation. Initially, four microphysics parameterizations are evaluated (Thompson, Morrison, WSM6 and WDM6). The Thompson parameterization produces the best results, while the other parameterizations generally overestimate the precipitation forecast, especially WDSM6. Additionally, the Thompson scheme tends to overestimate snow, while the Morrison scheme overestimates graupel. Regarding the data assimilation options, the results deteriorate and more spurious convection occurs when using a higher cycling frequency, i.e., 30 minutes instead of 60 minutes. The use of two outer loops produces worse precipitation forecasts than the use of one outer loop, and the null-echo assimilation is shown to be an effective way to suppress spurious convection. However, in some cases, the null-echo assimilation also removes convective clouds that are not observed by the radar and/or are still not producing rain, but have the potential to grow into an intense convective cloud with heavy rainfall. Finally, a cloud convective mask was implemented using ancillary satellite data to prevent null-echo assimilation from removing potential convective clouds. The mask demonstrated to be beneficial in some circumstances, but it needs to be carefully evaluated in more cases to have a more robust conclusion regarding its use.
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