Development and Evaluation of AMSU-A Cloud Detection over the Tibetan Plateau

Advanced Microwave Sounding Unit-A (AMSU-A) and Microwave Humidity Sounder (MHS) data have been widely assimilated in operational forecasting systems. However, effective distinction between cloudy and clear-sky data is still an essential prerequisite for the assimilation of microwave observations. Cloud detection over the Tibetan Plateau has long been a challenge owing to the influence of low temperatures, terrain height, surface vegetation, and inaccurate background fields. Based on the variations in the response characteristics of different channels of AMSU-A to clouds, five AMSU-A window and low-peaking channels (channels 1–4 and 15) are chosen to establish a cloud detection index. Combined with the existing MHS cloud detection index, a cloud detection scheme over the Tibetan Plateau is proposed. Referring to VISSR-II (Stretched Visible and Infrared Spin Scan Radiometer-II) and CALIPSO (The Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) cloud classification products, the detection rate of cloudy data and the rejection rate of clear-sky data under different cloud index thresholds are evaluated. Results show that the new cloud detection scheme can identify more than 80% of cloudy data on average, but this decreases to 72% for area with terrain higher than 5 km, and the false deletion rate remains stable at 45%. The detection rates of mixed clouds and cumulonimbus are higher than 90%, but it is lower than 50% for altostratus with an altitude of about 7–8 km. Comparative analysis shows that the new method is more suitable for areas with terrain higher than 700 m. Based on the cloud detection results, the effects of terrain height on the characteristics of observation error and bias are also discussed for AMSU-A channels 5 and 6.

[1]  Juan Li,et al.  Development and Evaluation of a New Method for AMSU-A Cloud Detection over Land , 2021, Remote. Sens..

[2]  M. Bilal,et al.  Comparison of MODIS- and CALIPSO-Derived Temporal Aerosol Optical Depth over Yellow River Basin (China) from 2007 to 2015 , 2020, Earth Systems and Environment.

[3]  X. Zou,et al.  Liquid water path retrieval using the lowest frequency channels of Fengyun-3C Microwave Radiation Imager (MWRI) , 2017, Journal of Meteorological Research.

[4]  Xiaolei Zou,et al.  Development and initial assessment of a new land index for microwave humidity sounder cloud detection , 2016, Journal of Meteorological Research.

[5]  Zhengkun Qin,et al.  Improved Tropical Storm Forecasts withGOES-13/15Imager Radiance Assimilation and Asymmetric Vortex Initialization in HWRF , 2015 .

[6]  John A. Knaff,et al.  Assimilating AMSU-A Radiances in the TC Core Area with NOAA Operational HWRF (2011) and a Hybrid Data Assimilation System: Danielle (2010) , 2013 .

[7]  Klaus Fraedrich,et al.  Variability of temperature in the Tibetan Plateau based on homogenized surface stations and reanalysis data , 2013 .

[8]  Guoxiong Wu,et al.  An assessment of summer sensible heat flux on the Tibetan Plateau from eight data sets , 2012, Science China Earth Sciences.

[9]  Xubin Zeng,et al.  Evaluation of multireanalysis products with in situ observations over the Tibetan Plateau , 2012 .

[10]  Peter Bauer,et al.  Observation errors in all‐sky data assimilation , 2011 .

[11]  P. Bauer,et al.  Satellite cloud and precipitation assimilation at operational NWP centres , 2011 .

[12]  Filipe Aires,et al.  A Land and Ocean Microwave Cloud Classification Algorithm Derived from AMSU-A and -B, Trained Using MSG-SEVIRI Infrared and Visible Observations , 2011 .

[13]  Vincent Guidard,et al.  Enhancements of Satellite Data Assimilation over Antarctica , 2010 .

[14]  Niels Bormann,et al.  Estimates of spatial and interchannel observation‐error characteristics for current sounder radiances for numerical weather prediction. I: Methods and application to ATOVS data , 2010 .

[15]  Yuping Yan,et al.  Relationship between temperature trend magnitude, elevation and mean temperature in the Tibetan Plateau from homogenized surface stations and reanalysis data , 2010 .

[16]  Florence Rabier,et al.  Global 4DVAR Assimilation and Forecast Experiments Using AMSU Observations over Land. Part I: Impacts of Various Land Surface Emissivity Parameterizations , 2010 .

[17]  Florence Rabier,et al.  Global 4DVAR Assimilation and Forecast Experiments Using AMSU Observations over Land. Part II: Impacts of Assimilating Surface-Sensitive Channels on the African Monsoon during AMMA , 2010 .

[18]  D. Winker,et al.  Overview of the CALIPSO Mission and CALIOP Data Processing Algorithms , 2009 .

[19]  A. McNally.  The direct assimilation of cloud‐affected satellite infrared radiances in the ECMWF 4D‐Var , 2009 .

[20]  D. Qin,et al.  Evaluation of precipitation from the ERA‐40, NCEP‐1, and NCEP‐2 Reanalyses and CMAP‐1, CMAP‐2, and GPCP‐2 with ground‐based measurements in China , 2009 .

[21]  D. Winker,et al.  A height resolved global view of dust aerosols from the first year CALIPSO lidar measurements , 2008 .

[22]  Weidong Guo,et al.  Calibrating and Evaluating Reanalysis Surface Temperature Error by Topographic Correction , 2008 .

[23]  James A. Jung,et al.  A Two-Season Impact Study of Four Satellite Data Types and Rawinsonde Data in the NCEP Global Data Assimilation System , 2008 .

[24]  Dick Dee,et al.  Adaptive bias correction for satellite data in a numerical weather prediction system , 2007 .

[25]  F. Rabier,et al.  Microwave land emissivity and skin temperature for AMSU‐A and ‐B assimilation over land , 2006 .

[26]  Peter Bauer,et al.  Implementation of 1D+4D‐Var assimilation of precipitation‐affected microwave radiances at ECMWF. II: 4D‐Var , 2006 .

[27]  Fu Congbin,et al.  Comparison of Products from ERA-40,NCEP-2,and CRU with Station Data for Summer Precipitation over China , 2006 .

[28]  K. Okamoto,et al.  The Assimilation of ATOVS Radiances in the JMA GIobal Analysis System , 2005 .

[29]  Catherine Prigent,et al.  Microwave land emissivity calculations using AMSU measurements , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[30]  Mark C. Serreze,et al.  Climate change and variability using European Centre for Medium‐Range Weather Forecasts reanalysis (ERA‐40) temperatures on the Tibetan Plateau , 2005 .

[31]  Ralf Bennartz,et al.  Precipitation analysis using the Advanced Microwave Sounding Unit in support of nowcasting applications , 2002 .

[32]  Fuzhong Weng,et al.  Retrieval of Ice Cloud Parameters Using the Advanced Microwave Sounding Unit , 2002 .

[33]  F. Aires,et al.  A new neural network approach including first guess for retrieval of atmospheric water vapor, cloud liquid water path, surface temperature, and emissivities over land from satellite microwave observations , 2001 .

[34]  Fuzhong Weng,et al.  Precipitation characteristics over land from the NOAA‐15 AMSU sensor , 2000 .

[35]  Fuzhong Weng,et al.  Retrieval of Ice Cloud Parameters Using a Microwave Imaging Radiometer , 2000 .

[36]  Ralph Ferraro,et al.  Special sensor microwave imager derived global rainfall estimates for climatological applications , 1997 .

[37]  J. R. Eyre,et al.  Assimilation of TOVS radiance information through one-dimensional variational analysis , 1993 .

[38]  X. Zou,et al.  Impact of AMSU-A Data Assimilation over High Terrains on QPFs Downstream of the Tibetan Plateau , 2019, Journal of the Meteorological Society of Japan. Ser. II.

[39]  P. Bauer,et al.  670 Assimilating AMSU-A temperature sounding channels in the presence of cloud and precipitation , 2012 .

[40]  Xue Jishan.  Scientific issues and perspective of assimilation of meteorological satellite data , 2009 .

[41]  Fu Cong,et al.  Preliminary Comparison and Analysis between ERA-40,NCEP-2 Reanalysis and Observations over China , 2006 .

[42]  Peter Bauer,et al.  488 Implementation of 1 D + 4 D-Var Assimilation of Precipitation Affected Microwave Radiances at ECMWF , Part II : 4 D-Var , 2006 .

[43]  P. Bauer,et al.  487 Implementation of 1 D + 4 D-Var Assimilation of Precipitation Affected Microwave Radiances at ECMWF , Part I : 1 D-Var , 2006 .

[44]  Start Regional,et al.  An Intercomparison between NCEP Reanalysisand Observed Data over China , 2004 .

[45]  Ecmwf Newsletter,et al.  EUROPEAN CENTRE FOR MEDIUM-RANGE WEATHER FORECASTS , 2004 .

[46]  P. Bauer,et al.  Implementation of 1D+4D‐Var assimilation of precipitation‐affected microwave radiances at ECMWF. I: 1D‐Var , 2022 .