Please wait a minute...

Frontiers of Earth Science

Front. Earth Sci.    2014, Vol. 8 Issue (2) : 251-263     DOI: 10.1007/s11707-014-0405-3
RESEARCH ARTICLE |
Impact of FY-3A MWTS radiances on prediction in GRAPES with comparison of two quality control schemes
Juan LI1,Xiaolei ZOU2,*()
1. National Meteorological Center, China Meteorological Administration, Beijing 100081, China
2. Department of Earth, Ocean and Atmospheric Sciences, Florida State University, Tallahassee 32306, USA
Download: PDF(1357 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks
Abstract

The impact of Microwave Temperature Sounder (MWTS) radiances on the prediction of the Chinese Numerical Weather prediction (NWP) system-GRAPES (Global and Regional Assimilation and PrEdiction System) with comparison of two Quality Control (QC) schemes was researched. The main differences between the two schemes are cloud detection, O–B (brightness temperature difference between observation and model simulation) check and thinning. To evaluate the impact of the two QC schemes on GRAPES, a typhoon case study and cycle experiments were conducted. In the typhoon case study, two experiments were conducted using both the new and old QC schemes. The results show that outliers are removed in the new QC while they exist in the old QC. The analysis and the model forecast are subsequently generated after assimilating data from the two QC schemes. The model-predicted steering flows more southward with the new QC scheme, and as a result, the forecast track in the experiments is more southward, i.e., closer to the best track than the old scheme. In addition to the case study, four impact cycle experiments were conducted for 25-day periods. The results show that the new QC scheme removed nearly all the biases whereas the old scheme could not. Furthermore, the mean and standard deviation of analysis increments with the new scheme is much smaller than those of O–B. In contrast, the old scheme values are either slightly smaller or the same. Verifications indicate that forecast skill is improved after applying the new scheme. The largest improvements are found in the Southern Hemisphere. According to the results above, MWTS with the new QC scheme can improve the GRAPES forecast.

Keywords FY-3      MWTS      typhoon      GRAPES     
Corresponding Authors: Xiaolei ZOU   
Issue Date: 24 June 2014
 Cite this article:   
Juan LI,Xiaolei ZOU. Impact of FY-3A MWTS radiances on prediction in GRAPES with comparison of two quality control schemes[J]. Front. Earth Sci., 2014, 8(2): 251-263.
 URL:  
http://journal.hep.com.cn/fesci/EN/10.1007/s11707-014-0405-3
http://journal.hep.com.cn/fesci/EN/Y2014/V8/I2/251
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
Juan LI
Xiaolei ZOU
Channel No.Center frequency/(GHz)Peak weighting function height /(hPa)NEΔT/K
150.227surface0.5
253.6567000.4
355.0203000.4
457.373900.4
Tab.1  Channel characteristics of FY-3A MWTS
Channel No.kσo
210.635
320.511
420.511
Tab.2  Variables used in O–B check in old scheme
Fig.1  Typhoon Ma-on (No.1106) best track with a decrease in intensity from 0600 UTC, 12 July to 0000UTC, 22 July 2011. Different symbols and colors represent different typhoon classifications and sea level pressures respectively. (TS: Tropical Storm; STS: Strong Tropical Storm; TY: Typhoon; STY: Strong Typhoon; Super TY: Super Typhoon).
Fig.2  Background field with (c–d) and without (a–b) bogus typhoon at 00UTC, 13 July 2011. (a, c) The wind vector, wind speed (shaded, m·s–1), and sea level pressure (contour, hPa) of background field; (b, d) vertical section of temperature anomaly (shaded, K) and meridional wind (m·s–1).
Fig.3  (a) Cloudy FOVs defined by cloud fraction>37% (green dots) and MetOp-A AMSU-A FOVs with LWP>0.05 kg·m–2 (black dots); (b) same as (a) but for LWP>0.01 kg·m–2; (c) clear FOVs defined by cloud fraction≤37% (blue dots)) and that removed by O–B check (red dots). MetOp-A AMSU-A FOVs with LWP≤0.05 kg·m–2 are shown as grey dots; (d) same as (c) but for LWP≤0.01 kg·m–2, in MWTS2 experiment from 2100UTC, 12 July to 0300UTC, 13 July 2011.
Fig.4  (a) Cloudy FOVs defined by |O–B|ch1≥3 K (green dots) and MetOp-A AMSU-A FOVs with LWP>0.05 kg·m–2 ( black dots); (b) same to (a) but for LWP>0.01 kg·m–2; (c) clear FOVs defined by |O–B|ch1<3 K (blue dots) and clear FOVs removed by O–B check (red dots). MetOp-A AMSU-A FOVs with LWP≤0.05 kg·m–2 are shown in grey dots; (d) same to (c) but for LWP≤0.01 kg·m–2, in MWTS1 experiment from 2100UTC, 12 July to 0300UTC, 13 July 2011.
Fig.5  (a) The wind vector and wind speed (shaded) of the mean flow of the initial forecast filed at 00 UTC 13 July in MWTS2 experiments. (b) The environmental flow derived from (a). (c) The environmental flow from MWTS1. (d) Difference of environmental flow between MWTS2 and MWTS1 (MWTS2-MWTS1). Hurricane symbols are used to indicate the location of the storm center. The black solid arrows in (b), (c), and (d) represent the steering flow from MWTS2 (4.3 m·s–1), MWTS1(3.8 m·s–1), and the difference of the steering flow between MWTS2 and MWTS1 (0.7 m·s–1), respectively.
Fig.6  (a) The wind vector, wind speed (shaded, m·s–1), and geopotential height (contour, gpm) of 300 hPa at 1800 UTC 13 July which is the 18-hour forecast of MWTS1. The black solid arrows represent the steering flow. (b) Same as (a) but for MWTS2. (c) Same as (a) but for 1200 UTC 15 July which is the 60-hour forecast of MWTS1. (d) Same as (c) but for MWTS2. The blue solid arrows represent the difference of steering flow between MWTS2 and MWTS1 in (a–b) (0.3 m·s–1) and in (c–d) (0.5 m·s–1).
Fig.7  (a) Best track and forecast track of CONV, MWTS1, MWTS2 experiments; (b) Forecast track error of CONV, MWTS1, and MWTS2 experiments; (c) Same as (b) but for sea level pressure.
EXPObservation Data
CONV1Conventional data+FY-3A MWTS (old scheme)
CONV2Conventional data+FY-3A MWTS (new scheme)
SAT1Conventional data+NOAA-15/18 AMSU-A+MetOp-A AMSU-A+COSMIC RO+FY-3A MWTS (old scheme)
SAT2Conventional data+NOAA-15/18 AMSU-A+MetOp-A AMSU-A+COSMIC RO+FY-3A MWTS (new scheme)
Tab.3  Experiment design for the four cycle experiments
Fig.8  Daily variations of MWTS data counts assimilated in CONV1, CONV2, SAT1, and SAT2 from 12 UTC, 7 July to 12UTC, 31 July 2011. (a) Channel 2; (b) Channel 3; (c) Channel 4
Fig.9  Daily variation of the bias (a) and STD (b) of O–B (solid line) and O–A (dashed line) for channel 2 in CONV1 (red) and CONV2 (blue).
Fig.10  Daily variation of the bias (a) and STD (b) of O–B (solid line) and O–A (dashed line) for channel 3 in CONV1 (red) and CONV2 (blue).
Fig.11  Daily variation of the bias (a) and STD (b) of O–B (solid line) and O–A (dashed line) for channel 4 in CONV1 (red) and CONV2 (blue).
Fig.12  Daily variation of the bias (a) and STD (b) of O–B (solid line) and O–A (dashed line) for channel 2 in SAT1 (red) and SAT2 (blue).
Fig.13  Daily variation of the bias (a) and STD (b) of O–B (solid line) and O–A (dashed line) for channel 3 in SAT1 (red) and SAT2 (blue).
Fig.14  Daily variation of the bias (a) and STD (b) of O–B (solid line) and O–A (dashed line) for channel 4 in SAT1 (red) and SAT2 (blue).
Fig.15  Mean ACC of 500 hPa geopotential height in Northern (a) and Southern (c) Hemispheres from 12 UTC, 7 July to 12 UTC, 31 July 2011. (b) and (d) are similar to (a) and (c) but for RMS.
1 AhnM H, KimM J, ChungC Y, SuhA S (2003). Operational implementation of the ATOVS processing procedure in KMA and its validation. Adv Atmos Sci, 20(3): 398–414
doi: 10.1007/BF02690798
2 BakerN L, DaleyR (2000). Observation and background adjoint sensitivity in the adaptive observation-targeting problem. Q J R Meteorol Soc, 126(565): 1431–1454
doi: 10.1002/qj.49712656511
3 BouttierF, KellyG (2001). Observing-system experiments in the ECMWF 4D-Var data assimilation system. Q J R Meteorol Soc, 127(574): 1469–1488
doi: 10.1002/qj.49712757419
4 CardinaliC (2009). Monitoring observation impact on short-range forecast. Q J R Meteorol Soc, 135(638): 239–250
doi: 10.1002/qj.366
5 CarrL E, ElsberryR L (1990). Observational evidence for predictions of tropical cyclone propagation relative to environmental steering. J Atmos Sci, 47(4): 542–548
6 ChenD H, XueJ S, YangX S, ZhangH L, ShenX S, HuJ L, WangY, JiL R, ChenJ B (2008). New generation of multi-scale NWP system (GRAPES): general scientific design. Chin Sci Bull, 53(22): 3433–3445
doi: 10.1007/s11434-008-0494-z
7 DerberJ C, WuW S (1998). The use of TOVS cloud-cleared radiances in the NCEP SSI analysis system. Mon Weather Rev, 126(8): 2287–2299
doi: 10.1175/1520-0493(1998)126<2287:TUOTCC>2.0.CO;2
8 DongC H, YangJ, YangZ D, LuN M, ShiJ M, ZhangP, LiuY J, CaiB, ZhangW (2009). An overview of a new Chinese weather satellite FY-3A. Bull Am Meteorol Soc, 90(10): 1531–1544
doi: 10.1175/2009BAMS2798.1
9 FourriéN, DoerenbecherA, BergotT, JolyA (2002). Adjoint sensitivity of the forecast to TOVS observations. Q J R Meteorol Soc, 128(586): 2759–2777
doi: 10.1256/qj.01.167
10 KuriharaY, BenderM A, RossR J (1993). An initialization scheme of typhoon Models by vortex specification. Mon Weather Rev, 121(7): 2030–2045
doi: 10.1175/1520-0493(1993)121<2030:AISOHM>2.0.CO;2
11 LanglandR H, BakerA L (2004). Estimation of observation impact using the NRL atmospheric variational data assimilation adjoint system. Tellus, Ser A, Dyn Meterol Oceanogr, 56(3): 189–201
doi: 10.1111/j.1600-0870.2004.00056.x
12 LansanteJ R (1996). Resistant, robust and non-parametric techniques for the analysis of climate data: theory and examples, including applications to historical radiosonde station data. Int J Climatol, 16(11): 1197–1226
doi: 10.1002/(SICI)1097-0088(199611)16:11<1197::AID-JOC89>3.0.CO;2-L
13 LiJ, ZouX (2013). A Quality Control Procedure for FY-3A MWTS Measurements with Emphasis on Cloud Detection Using VIRR Cloud Fraction. J Atmos Ocean Technol, 30: 1704–1715
doi: 10.1175/JTECH-D-12-00164.1
14 LuQ, BellW, BauerP, BormannN, PeubeyC (2010). An Initial Evaluation of FY-3A Satellite Data. ECMWF Technical Memoranda No.631, ECMWF, Shinfield Park, Reading, UK, ECMWF, 58
15 MathurM B (1991). The National Meteorological Center’s Quasi-Lagrangian Model for hurricane prediction. Mon Weather Rev, 119(6): 1419–1447
doi: 10.1175/1520-0493(1991)119<1419:TNMCQL>2.0.CO;2
16 McNallyA P, DerberJ C, WuW, KatzB B (2000). The use of TOVS level-1b radiances in the NCEP SSI analysis system. Q J R Meteorol Soc, 126(563): 689–724
doi: 10.1002/qj.49712656315
17 OkamotoK, KazumoriM, OwadaH (2005). The assimilation of ATOVS radiances in the JMA global analysis system. J Meteorol Soc Jpn, 83(2): 201–217
doi: 10.2151/jmsj.83.201
18 RSMC Tokyo-Typhoon Center (2012). Summary of the 2011 typhoon season. Forty forth session, Hangzhou, China, ESCAP/WMO Typhoon Committee
19 VeldenC S, LeslieL M (1991). The basic relationship between tropical cyclone intensity and the depth of the environmental steering layer in the Australian region. Wea Forecasting, 6: 244–253
doi: 10.1175/1520-0434(1991)006<0244:TBRBTC>2.0.CO;2
20 WangG M, WangS W, LiuJ J (1996). A bogus typhoon scheme and its application to a movable nested mesh model. Journal of Tropical Meteorology, 12(1): 9–17 (in Chinese)
21 WangX, ZouX L, Weng F Z, You R (2012). Using NWP models to remove frequency shift induced bias in FY-3A MWTS measurements. IEEE Trans Geosci Rem Sens, 50(12): 4860–4874
doi: 10.1109/TGRS.2012.2200687
22 WengF, GrodyN C (1994). Retrieval of cloud liquid water using special sensor microwave imager (SSM/I). J Geophys Res, 99(D12): 25535–25551
doi: 10.1029/94JD02304
23 WuY H, ZouX (2008). Test of a simple approach for using TOMS total ozone data in hurricane environment. Q J R Meteorol Soc, 134(635): 1397–1408
doi: 10.1002/qj.299
24 XueJ S, ZhuangS Y, ZhuG F, ZhangH, LiuZ Q, LiuY, ZhuangZ R (2008). Scientific design and preliminary results of three-dimensional variational data assimilation system of GRAPES. Chin Sci Bull, 53(22): 3446–3457
doi: 10.1007/s11434-008-0416-0
25 YangJ, DongC H, LuN M, YangZ D, ShiJ M, ZhangP, LiuY J, CaiB (2009). FY-3A: the new generation polar-orbiting meteorological satellite of China. Acta Meteorologica Sinica, 67(4): 501–509 (in Chinese)
26 YouR, GuS Y, GuoY, WuX B, YangH, ChenW X (2012). Long-term calibration and accuracy assessment of the FengYun-3 Microwave Temperature Sounder radiance measurements. IEEE Trans Geosci Rem Sens, 50(12): 4854–4859
doi: 10.1109/TGRS.2012.2200257
27 ZhangP, YangJ, DongC H, LuN M, YangZ D, ShiJ M (2009). General introduction on payloads, ground segment and data application of Fengyun 3A. Frontiers of Earth Science, 3(3): 367–373
doi: 10.1007/s11707-009-0036-2
28 ZouX, WangX, WengF, LiG (2011). Assessments of Chinese FengYun Microwave Temperature Sounder (MWTS) measurements for weather and climate applications. J Atmos Ocean Technol, 28(10): 1206–1227
doi: 10.1175/JTECH-D-11-00023.1
29 ZouX, ZengZ (2006). A quality control procedure for GPS radio occultation data. J Geophys Res, 111(D2): D02112
doi: 10.1029/2005JD005846
Related articles from Frontiers Journals
[1] Juan LI,Guiqing LIU. Assimilation of Chinese Fengyun-3B Microwave Temperature Sounder radiances into the Global GRAPES system with an improved cloud detection threshold[J]. Front. Earth Sci., 2016, 10(1): 145-158.
[2] Liang WANG, Xiaodong ZHAO, Yongming SHEN. Coupling hydrodynamic models with GIS for storm surge simulation: application to the Yangtze Estuary and the Hangzhou Bay, China[J]. Front Earth Sci, 2012, 6(3): 261-275.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed