Application of IMAPP at East China Normal University
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1 CSPP / IMAPP Users Group Meeting Application of IMAPP at East China Normal University Yan an Liu 1, 2, Wei Gao 1, Runhe Shi 1, Allen Huang 2, Kathy Strabala 2, Liam Gumley 2, Yunzhu Chen 1, Xiaoyun Zhuang 1 1. East China Normal University, Shanghai, China 2. CIMSS/SSEC, University of Wisconsin- Madison, WI, USA 5/23/2013
2 Outline 1 Installation & Training 2 Localization of profile retrieval 3 Data fusion of AOD 4 Evaluation of DBCRAS
3 Installation and Training Site Location 85m Science Building A at ECNU
4 Installation and Training Onsite installation Lifting of positioner Lifting of reflector
5 Installation and Training Onsite installation Outdoor installation Indoor installation EOF-FES DBPS Archive System Dehydrator
6 Installation and Training Hardware onsite training Software training
7 Installation and Training Remote control UI
8 First ECNU EOS-FES/DBPS Terra MODIS Images(0235 UTC, 25 May, 2010) Infrared Visible True color True Color
9 First ECNU EOS-FES/DBPS Terra MODIS Image in Google Earth
10 First DB Products Terra/MODIS Retrieval products Sea Surface Temperature Aerosol Optical Thickness Water Vapor
11 Localization of Temperature Profile Retrieval Distribution of testing samples in global and China. Comparison between the two algorithms: 1. Dual-Regression algorithm based on global regression coefficients; 2. AIRS/MODIS retrieval algorithm that uses both AIRS L1B data and MODIS product. To get a better atmospheric temperature profile, proposed a regional algorithm: 3. Dual-Regression algorithm based on Chinese regression coefficients.
12 Localization of Temperature Profile Retrieval Dual-regression AIRS/MODIS retrieval Chinese coefficient
13 Localization of Temperature Profile Retrieval
14 Localization of Temperature Profile Retrieval
15 Localization of Temperature Profile Retrieval Averaged absolute error and relative error compared with radiosonde observations: 1.Dual-regression algorithm: 1.71K, 0.84% 2.AIRS/MODIS Retrieval algorithm: 2.68K, 1.32% 3. Chinese coefficients: 1.59K, 0.78%
16 Data Fusion of Aerosol Optical Depth Satellite retrieved aerosol product has the low spatial coverage because of the limitation of cloud coverage and dark target algorithm. Horizontal meteorological visibility (HMV) is another very important parameter in describing aerosol optical characteristics. It is observed fixed times and published per day by widely distributed meteorological sites. It can be a good supplement of satellite retrieved AOD. Thus, a fast fusion algorithm was developed. Terra and Aqua satellite MODIS AOD and ground-based HMV data were fused in order to obtain AOD product with high spatial resolution and complete spatial coverage in real time. Step 1 : Linear fusion Step 2 : Model conversion fusion Step 3 : Interpolation
17 Data Fusion of Aerosol Optical Depth (a) Terra (b) Aqua Distribution of AOD at east of China
18 Data Fusion of Aerosol Optical Depth Spatial coverage after linear fusion Source of AOD data
19 Data Fusion of Aerosol Optical Depth HMV Peterson Model AOD Distribution of HMV data from observation sites (stars) Model conversion fusion
20 Data Fusion of Aerosol Optical Depth Without HMV After model conversion Thin plate splines interpolation
21 Data Fusion of Aerosol Optical Depth Using cross-validation method to validate the fusion steps separately Fusion step RMSE Step1: Linear fusion 0.11 Step2: Model conversion fusion 0.31 Step3: Interpolation 0.35
22 Application of IDEA-I Infusing satellite Data into Environmental Application - International
23 Evaluation of DBCRAS Direct Broadcast CIMSS Regional Assimilation System Resolution: Sigma levels: 38 Grid Size: Time-step: 48 km;16 km 210 x140; 207x seconds Forecast length: 72 hours; 48 hours Initialization: Output: Initial times (UTC): 12-hr spin-up with 5-7 MODIS inserts 3 hourly, grib2 format 00/12 UTC Initial conditions: 1/2 degree GFS, 6 hr Forecast Boundaries: Inputs: 6 hourly, one degree GFS Forecasts Surface, RAOBs, Re-locate (31 N, 121 E) MODIS: Total precipitable water, cloud-top pressure
24 Evaluation of DBCRAS 72h forecast at 500hPa, 12UTC 27/10/2010
25 Evaluation of DBCRAS Methods Qualitative and quantitative analysis Time 00UTC, heavy rain case at Yangtze river delta in June 2011 Data CRAS output (format: grib2); Projection: Polar stereographic Resolution: ECMWF: ERA-Interim (format: netcdf) Projection: Cylindrical equidistant Resolution: Other data: Radiosonde; TRMM precipitation product
26 Evaluation of DBCRAS 24h forecast temperature bias (a) 300hPa (b) 500hPa (c) 700hPa (d) 850hPa
27 Evaluation of DBCRAS 24h forecast relativity humidity bias (a) 300hPa (b) 500hPa (c) 700hPa (d) 850hPa
28 Evaluation of DBCRAS
29 Evaluation of DBCRAS
30 Evaluation of DBCRAS Forecast of 24h accumulated precipitation compared with TRMM precipitation
31 Researches supported by IMAPP Xiaoyun Zhuang : Localization of temperature profile retrieval Yunzhu Chen : Data fusion of aerosol optical depth Cong Zhou : Research on CO2 Yuanyuan Chen : PM 10 forecast Yan an Liu : Assimilation of CrIS in regional model
32
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