Investigation of features of May, 2001 tropical cyclone over the Arabian Sea through IRS-P4 and other satellite data

In this paper, utility of satellite derived atmospheric motion vectors and geophysical parameters is brought out to discern appropriate signals for improving short-range forecasts in respect of development/dissipation of tropical cyclones over the Indian region. Results of a particular case study of May, 2001 cyclone, which formed in the Arabian Sea are reported. Analysis of wind field with input of modified cloud motion vectors and water vapour wind vectors is performed utilizing Optimum Interpolation (OI) technique at 850 and 200 hPa for finding dynamical changes such as vorticity, convergence and divergence for the complete life period of this cyclone. Simultaneously, variations in geophysical parameters obtained from IRS-P4 and TRMM satellites in ascending and descending nodes are compared with dynamical variations for discerning some positive signals to improve short range forecasts over the Indian region. The enhancement of cyclonic vorticity at 200 hPa over larger area surrounding center of cyclone was observed from 26 to 28 May 2001 which gave a positive signal for dissipation of storm.

Prediction of Solar Power Using Near-Real Time Satellite Data

Solar energy production is affected by the attenuation of incoming irradiance from underlying clouds. Often, improvements in the short-term predictability of irradiance using satellite irradiance models can assist grid operators in managing intermittent solar-generated electricity. In this paper, we develop and test a satellite irradiance model with short-term prediction capabilities using cloud motion vectors. Near-real time visible images from Himawari-8 satellite are used to derive cloud motion vectors using optical flow estimation techniques. The cloud motion vectors are used for the advection of pixels at future time horizons for predictions of irradiance at the surface. Firstly, the pixels are converted to cloud index using the historical satellite data accounting for clear, cloudy and cloud shadow pixels. Secondly, the cloud index is mapped to the clear sky index using a historical fitting function from the respective sites. Thirdly, the predicated all-sky irradiance is derived by scaling the clear sky irradiance with a clear sky index. Finally, a power conversion model trained at each site converts irradiance to power. The prediction of solar power tested at four sites in Australia using a one-month benchmark period with 5 min ahead prediction showed that errors were less than 10% at almost 34–60% of predicted times, decreasing to 18–26% of times under live predictions, but it outperformed persistence by >50% of the days with errors <10% for all sites. Results show that increased latency in satellite images and errors resulting from the conversion of cloud index to irradiance and power can significantly affect the forecasts.

A New Approach for Satellite-Based Probabilistic Solar Forecasting with Cloud Motion Vectors

Probabilistic solar forecasting is an issue of growing relevance for the integration of photovoltaic (PV) energy. However, for short-term applications, estimating the forecast uncertainty is challenging and usually delegated to statistical models. To address this limitation, the present work proposes an approach which combines physical and statistical foundations and leverages on satellite-derived clear-sky index (kc) and cloud motion vectors (CMV), both traditionally used for deterministic forecasting. The forecast uncertainty is estimated by using the CMV in a different way than the one generally used by standard CMV-based forecasting approach and by implementing an ensemble approach based on a Gaussian noise-adding step to both the kc and the CMV estimations. Using 15-min average ground-measured Global Horizontal Irradiance (GHI) data for two locations in France as reference, the proposed model shows to largely surpass the baseline probabilistic forecast Complete History Persistence Ensemble (CH-PeEn), reducing the Continuous Ranked Probability Score (CRPS) between 37% and 62%, depending on the forecast horizon. Results also show that this is mainly driven by improving the model’s sharpness, which was measured using the Prediction Interval Normalized Average Width (PINAW) metric.

Climate data record of atmospheric motion vectors at EUMETSAT for usage in reanalysis

&lt;p&gt;This presentation provides an overview of the different upper-air wind data records available at EUMETSAT for usage in global and regional reanalysis. The assimilation of Atmospheric Motion Vectors (AMV) is recognised to be important to reduce the forecast errors in NWP model runs. In support of the Copernicus Climate Change Service (C3S), EUMETSAT produced several AMV Climate Data Records (CDR) from geostationary and low-earth orbit satellites for assimilation into ECMWF&amp;#8217;s next global reanalysis ERA6.&lt;/p&gt;&lt;p&gt;Since the launch of its first generation of geostationary satellites, EUMETSAT has developed its own unique algorithms to derive atmospheric motion vectors (AMVs). These algorithms are used to provide real time AMVs using images acquired from instruments on-board both polar and geostationary satellites. These AMVs are routinely assimilated into weather forecast models. EUMETSAT archived all image data from its instruments (MVIRI and SEVIRI) in geostationary orbit and the global record of Advanced Very High Resolution Radiometer (AVHRR) data back to the late 1970s providing a suitable data source for climate research allowing the production of consistent AMV CDRs over the entire period.&lt;/p&gt;&lt;p&gt;Two long AMV data records are available now from the geostationary sensors on Meteosat-2 to Meteosat-10 covering 1981-2017 over Africa and Europe and from AVHRR Global Area Coverage (GAC) data from 16 AVHRR instruments starting with the TIROS-N satellite and covering polar AMVs over the Northern and Southern hemisphere from 1978-2019. In addition, full resolution AVHRR images (Local Area Coverage (LAC)) from the AVHRR aboard the polar orbiting Metop-A and -B satellites were used to generate a CDR containing polar AMVs from single satellite retrievals and global AMVs from the combined Metop-A/B dual satellite retrieval starting in 2007 and 2013, respectively.&lt;/p&gt;&lt;p&gt;For all data records, the EUMETSAT AMV algorithm adapted for climate purposes was used and extensive validation of the data records were performed. It shows that the CDR are homogeneous and very stable over the period. They are suitable for usage in model reanalysis and climate analysis. The CDR are in agreement with ground based radiosonde and model data. For the polar AMVs, a remarkable agreement with MODIS AMVs has been found.&lt;/p&gt;&lt;p&gt;To better serve closer to real time needs for reanalysis, EUMETSAT is experimenting with the continuous production of an Interim Climate Data Record (ICDR) with a timeliness close to real-time. With a still not completely operational low-cost approach, a timeliness of 83% within 18 hours at similar quality was achieved.&lt;/p&gt;&lt;p&gt;In addition to the existing data records the presentation provides the plan for future improvements and new CDR releases for AMV data records in the coming years. In particular, the use of better information on multi-layer cloud objects in AMV retrievals is a central part for the improvements of the AMVs from geostationary orbit.&amp;#160;&amp;#160;&lt;/p&gt;