Derivation of Cloud-Free-Region Atmospheric Motion Vectors from FY-2E Thermal Infrared Imagery

The operational cloud-motion tracking technique fails to retrieve atmospheric motion vectors （AMVs） in areas lacking cloud; and while water vapor shown in water vapor imagery can be used, the heights assigned to the retrieved AMVs are mostly in the upper troposphere. As the noise-equivalent temperature difference （NEdT） performance of FY-2E split win- dow （10.3-11.5 μm, 11.6-12.8 μm） channels has been improved, the weak signals representing the spatial texture of water vapor and aerosols in cloud-free areas can be strengthened with algorithms based on the difference principle, and applied in calculating AMVs in the lower troposphere. This paper is a preliminary summary for this purpose, in which the principles and algorithm schemes for the temporal difference, split window difference and second-order difference （SD） methods are introduced. Results from simulation and cases experiments are reported in order to verify and evaluate the methods, based on comparison among retrievals and the ＂truth＂. The results show that all three algorithms, though not perfect in some cases, generally work well. Moreover, the SD method appears to be the best in suppressing the surface temperature influence and clarifying the spatial texture of water vapor and aerosols. The accuracy with respect to NCEP 800 hPa reanalysis data was found to be acceptable, as compared with the accuracy of the cloud motion vectors.

Characteristics of Fengyun-4A Satellite Atmospheric Motion Vectors and Their Impacts on Data Assimilation

The high observation efficiency,scanning speed and observation frequency of the Fengyun-4A(FY-4A)satellite indicates the progress of Chinese geostationary meteorological satellites.The characteristics of FY-4A atmospheric motion vectors(AMVs)derived from the high-level water vapor(WV-High)channel,mid-level water vapor(WV-Mid)channel,and infrared(IR)channel of FY-4A are analyzed,and their corresponding observation errors estimated.Then,the impacts of single-channel and multi-channel FY-4A AMVs on RMAPS-ST(the Rapid-refresh Multi-scale Analysis and Prediction System-Short Term)are evaluated based on one-month data assimilation cycling and forecasting experiments.Results show that the observation errors of FY-4A AMVs from the three channels have an explicit vertical structure.Results from the cycling experiments indicate that the assimilation of AMVs from WV-High produces more apparent improvement of the wind in the upper layer,while a more positive effect in the lower layer is achieved by the assimilation of AMVs from IR.Furthermore,the assimilation of AMVs from IR is more skillful for medium and moderate precipitation than from other channels owing to the good quality of data in the lower layer in the AMVs from IR.Assimilation of FY-4A AMVs from the three channels could combine the advantages of assimilation from each individual channel to improve the wind in the upper,middle and lower layers simultaneously.

Water Vapor Motion Signal Extraction from FY-2E Longwave Infrared Window Images for Cloud-free Regions:The Temporal Difference Technique

The aim of this study is to calculate the low-level atmospheric motion vectors （AMVs） in clear areas with FY-2E IR2 window （11.59-12.79 μm） channel imagery,where the traditional cloud motion wind technique fails.A new tracer selection procedure,which we call the temporal difference technique,is demonstrated in this paper.This technique makes it possible to infer low-level wind by tracking features in the moisture pattern that appear as brightness temperature （TB） differences between consecutive sequences of 30-min-interval FY-2E IR2 images over cloud-free regions.The TB difference corresponding to a 10％ change in water vapor density is computed with the Moderate Resolution Atmospheric Transmission （MODTRAN4） radiative transfer model.The total contribution from each of the 10 layers is analyzed under four typical atmospheric conditions：tropical,midlatitude summer,U.S.standard,and midlatitude winter.The peak level of the water vapor weighting function for the four typical atmospheres is assigned as a specific height to the TB ＂wind＂.This technique is valid over cloudfree ocean areas.The proposed algorithm exhibits encouraging statistical results in terms of vector difference （VD）,speed bias （BIAS）,mean vector difference （MVD）,standard deviation （SD）,and root-mean-square error （RMSE）,when compared with the wind field of NCEP reanalysis data and rawinsonde observations.

Pre-Processing,Quality Assurance,and Use of Global Atmospheric Motion Vector Observations in CRA

Assimilation of atmospheric motion vectors(AMVs)is important in the initialization of the atmospheric state in numerical weather prediction models,especially over oceans and at high latitudes where conventional data are sparse.This paper presents a detailed description of the pre-processing,quality assurance,and use of global AMVs in China’s first generation of the 40-yr(1979-2018)CRA global atmospheric reanalysis product.A new AMV archive is integrated from near real-time operational Global Telecommunication System data and reprocessed AMV datasets released or produced mainly during 2014-2016 according to a priority principle.To avoid the misuse of data with systematic quality problems,the observations of all 18 types of AMVs from 54 satellites are pre-evaluated over the whole time series.The pre-evaluation system developed by the CRA team is based on the NCEP Gridpoint Statistical Interpolation(GSI)three-dimensional variational assimilation system and the ERA-Interim reanalysis product.The AMVs in the new AMV archive are denser than the AMVs prepared for the Climate Forecast System Reanalysis product,the bias and root-mean-square values are smaller,and the time series are steadier.The new AMV archive is assimilated in the CRA product based on the NCEP GSI assimilation procedure and quality control configuration with reference to the pre-evaluation results.This is the first time that the reprocessed AMVs from Fengyun-2 satellites from June 2005 to July 2017 are assimilated in a reanalysis product.The assimilation features inspire confidence in the accuracy and stability of these data.The mean root-mean-square values of the observation minus analysis infrared,water vapor,and visible AMV were 1.5-3.4,2.7-3.6,and 1.3-2.1 m s-1,respectively.This experience of integrating,pre-evaluating,and assimilating AMV observations is valuable for the next generation of reanalysis products.