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.

Exploiting Aeolus Level-2B Winds to Better Characterize Atmospheric Motion Vector Bias and Uncertainty

The need for highly accurate atmospheric wind observations is a high priority in the science community, and in particular numerical weather prediction (NWP). To address this requirement, this study leverages Aeolus wind LIDAR Level-2B data provided by the European Space Agency (ESA) to better characterize atmospheric motion vector (AMV) bias and uncertainty, with the eventual goal of potentially improving AMV algorithms. AMV products from geostationary (GEO) and low-Earth polar orbiting (LEO) satellites are compared with reprocessed Aeolus horizontal line-of-sight (HLOS) global winds observed in August and September 2019. Winds from two of the four Aeolus observing modes are utilized for comparison with AMVs: Rayleigh-clear (derived from the molecular scattering signal) and Mie-cloudy (derived from particle scattering). For the most direct comparison, quality controlled (QC’d) Aeolus winds are collocated with quality controlled AMVs in space and time, and the AMVs are projected onto the Aeolus HLOS direction. Mean collocation differences (MCD) and standard deviation (SD) of those differences (SDCD) are determined from comparisons based on a number of conditions, and their relation to known AMV bias and uncertainty estimates is discussed. GOES-16 and LEO AMV characterizations based on Aeolus winds are described in more detail. Overall, QC’d AMVs correspond well with QC’d Aeolus HLOS wind velocities (HLOSV) for both Rayleigh-clear and Mie-cloudy observing modes, despite remaining biases in Aeolus winds after reprocessing. Comparisons with Aeolus HLOSV are consistent with known AMV bias and uncertainty in the tropics, NH extratropics, and in the Arctic, and at mid- to upper-levels in both clear and cloudy scenes. SH comparisons generally exhibit larger than expected SDCD, which could be attributed to height assignment errors in regions of high winds and enhanced vertical wind shear. GOES-16 water vapor clear-sky AMVs perform best relative to Rayleigh-clear winds, with small MCD (-0.6 m s-1 to 0.1 m s-1) and SDCD (5.4–5.6 m s-1) in the NH and tropics that fall within the accepted range of AMV error values relative to radiosonde winds. Compared to Mie-cloudy winds, AMVs exhibit similar MCD and smaller SDCD (~4.4–4.8 m s-1) throughout the troposphere. In polar regions, Mie-cloudy comparisons have smaller SDCD (5.2 m s-1 in the Arctic, 6.7 m s-1 in the Antarctic) relative to Rayleigh-clear comparisons, which are larger by 1–2 m s-1. The level of agreement between AMVs and Aeolus winds varies per combination of conditions including the Aeolus observing mode coupled with AMV derivation method, geographic region, and height of the collocated winds. It is advised that these stratifications be considered in future comparison studies and impact assessments involving 3D winds. Additional bias corrections to the Aeolus dataset are anticipated to further refine the results.

The Potential of Satellite Sounding Observations for Deriving Atmospheric Wind in All-Weather Conditions

Atmospheric wind is an essential parameter in the global observing system. In this study, the water vapor field in Typhoon Lekima and its surrounding areas simulated by the Weather Research and Forecasting (WRF) model is utilized to track the atmospheric motion wind through the Farneback Optical Flow (OF) algorithm. A series of experiments are conducted to investigate the influence of temporal and spatial resolutions on the errors of tracked winds. It is shown that the wind accuracy from tracking the specific humidity is higher than that from tracking the relative humidity. For fast-evolving weather systems such as typhoons, the shorter time step allows for more accurate wind retrievals, whereas for slow to moderate evolving weather conditions, the longer time step is needed for smaller retrieval errors. Compared to the traditional atmospheric motion vectors (AMVs) algorithm, the Farneback OF wind algorithm achieves a pixel-wise feature tracking and obtains a higher spatial resolution of wind field. It also works well under some special circumstances such as very low water vapor content or the region where the wind direction is parallel to the moisture gradient direction. This study has some significant implications for the configuration of satellite microwave sounding missions through their derived water vapor fields. The required temporal and spatial resolutions in the OF algorithm critically determine the satellite revisiting time and the field of view size. The brightness temperature (BT) simulated through Community Radiative Transfer Model (CRTM) is also used to track winds. It is shown that the error of tracking BT is generally larger than that of tracking water vapor. This increased error may result from the uncertainty in simulations of brightness temperatures at 183 GHz.

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

<p>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’s next global reanalysis ERA6.</p><p>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.</p><p>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.</p><p>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.</p><p>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.</p><p>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.  </p>