Impact of <i>FY-3D</i> MWRI Radiance Assimilation in GRAPES 4DVar on Forecasts of Typhoon Shanshan

Hongyi XIAO; Wei HAN; Hao WANG; Jincheng WANG; Guiqing LIU; Changshan XU

doi:10.1007/s13351-020-9122-x

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Impact of FY-3D MWRI Radiance Assimilation in GRAPES 4DVar on Forecasts of Typhoon Shanshan

Hongyi XIAO^{1, 2,},
Wei HAN^{1, 2, ,},
Hao WANG^{1, 2,},
Jincheng WANG^{1, 2,},
Guiqing LIU^{1, 2,},
Changshan XU^3,

+ Author Affiliations + Find other works by these authors

Corresponding author: Wei HAN, hanwei@cma.gov.cn;

Funds:

Supported by the National Natural Science Foundation of China (41675108), National Key Research and Development Program (2018YFC1506700), and Second Tibetan Plateau Scientific Expedition and Research Program (2019QZKK0105)

DOI: 10.1007/s13351-020-9122-x

Abstract

Abstract

In this study, Fengyun-3D (FY-3D) MicroWave Radiation Imager (MWRI) radiance data were directly assimilated into the Global/Regional Assimilation and PrEdiction System (GRAPES) four-dimensional variational (4DVar) system. Quality control procedures were developed for MWRI applications by using algorithms from similar microwave instruments. Compared with the FY-3C MWRI, the bias of FY-3D MWRI observations did not show a clear node-dependent difference from the numerical weather prediction background simulation. A conventional bias correction approach can therefore be used to remove systematic biases before the assimilation of data. After assimilating the MWRI radiance data into GRAPES, the geopotential height and humidity analysis fields were improved relative to the control experiment. There was a positive impact on the location of the subtropical high, which led to improvements in forecasts of the track of Typhoon Shanshan.

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Fig. 8. The 120-h typhoon track forecasts given by (a) CTL1 and EXP1, (b) CTL2 and EXP2, and (c) CTL3 and EXP3. The forecasts given by the control and sensitivity experiments are plotted as squares and triangles, respectively. The observed typhoon track is represented by circles. The time is marked along the track for each 24 h.

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Fig. 1. Mean (O–B) of each channel for the ascending (curve with solid square) and descending (curve with empty circle) data for the FY-3D MWRI. Statistics were calculated for the 2-day data after applying the quality control procedures described in Section 3.2.

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Fig. 2. Flow chart showing the experiment setup used in this study. The experiment contains a 1-day cycle assimilation module and a 5-day forecast module. The abbreviation “AN” represents an analysis field.

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Fig. 3. OMB probability density function (PDF) profiles of the (a) 19-V, (b) 24-V, and (c) 37-V channels. The shadow/black bars correspond to the PDF of the OMB before/after the quality control (QC) procedures.

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Fig. 4. Results of the quality control experiments for the 37-V channel. (a) FY-3D MERSI image of Typhoon Shanshan on 5 August 2018. (b) The pixels contaminated by oceanic clouds, which are detected by the cloud detection scheme in Section 3.2, and the pixels with LWP > 0.1 mm are plotted. (c) Image of the brightness temperature (T_b) in the MWRI 37-V channel at 0600 UTC 5 August within a ±3-h time window from the observations without quality control. (d) The remaining pixels after quality control of the data in part (c). (e) The background T_b (K) corresponding to part (c). (f) The OMB (K) corresponding to part (d).

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Fig. 5. Difference of T_b between the 19-V and 24-V channels around North America on 4 August 2018.

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Fig. 6. OMB PDF profiles of the (a) 19-V, (b) 24-V, and (c) 37-V channels. The shadow/black bars correspond to the PDF of the OMB before/after the bias correction (BC) procedures. The solid and dashed lines are the fitted normal distribution profiles calculated by the black and shadow bars, respectively.

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Fig. 7. Specific humidity analysis increments in the (a, b) 850-hPa and (c, d) 23°N sections from the (a, c) EXP3 and (b, d) CTL3 experiments.

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Fig. 9. The subtropical high represented by the contour line of 588 dagpm at 500 hPa given by (a) observational results in MICAPS (Meteorological Information Comprehensive Analysis and Process System) and (b) the forecast results in CTL3 and EXP3 at 72 h after forecast initialization. In (a), the Asian low-pressure trough is marked by the red arrowheads. In (b), the contour line of 587.1 dagpm at the same height and same time is also plotted because of some unavoidable deviation; the results of CTL3 and EXP3 are shown by dashed green line and solid blue line, respectively.

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Table 1 FY-3D MWRI channel characteristics (see http://satellite.nsmc.org.cn/PortalSite/StaticContent/DeviceIntro_FY-3_MWRI.aspx)

Channel characteristic	Frequency (GHz)
Channel characteristic	10.65		18.7		23.8		36.5		89.0
Polarization	V	H	V	H	V	H	V	H	V	H
Abbreviation	10-V	10-H	19-V	19-H	24-V	24-H	37-V	37-H	89-V	89-H
Bandwidth (MHz)	180		200		400		400		3000
NEΔT (K)	0.5		0.5		0.5		0.5		0.8
IFOV (km²)	51 × 85		30 × 50		27 × 45		18 × 30		9 × 15
Pixel (km²)	40 × 11.2		40 × 11.2		20 × 11.2		20 × 11.2		10 × 11.2
Range (K)	3–340
Number of scan positions	266
Scanning technique	Conical
Swath width (km)	1400
Zenith angle (°)	45 ± 0.1
Incident angle (°)	53.1
Scan rate (s)	1.8 ± 0.1
Note: NEΔT: noise-equivalent brightness temperature (T_b).

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Table 2 Coefficients ${a_0}$ , ${a_1}$ , and ${a_2}$ in Eq. (6) for the MWRI

LWP_chan	a₀	a₁	a₂
10-V	−3.87	4.47	0.09
19-V	−1.94	3.03	0.37
37-V	−0.97	2.74	0.39
89-H	−0.37	−3.08	1.68

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Table 3 Threshold of the LWP corresponding to the three channels assimilated in this study

Channel	10-V	19-V	37-V
LWP (mm)	0.3	0.25	0.1

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Xiao, H. Y., W. Han, H. Wang, et al., 2020: Impact of FY-3D MWRI radiance assimilation in GRAPES 4DVar on forecasts of Typhoon Shanshan. J. Meteor. Res., 34(4), 836–850, doi: 10.1007/s13351-020-9122-x.

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Manuscript History

Received: 14 November 2019

Available online: 19 May 2020

Final form: 19 April 2020

Issue in Progress: 24 June 2020

Published online: 26 August 2020

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Impact of FY-3D MWRI Radiance Assimilation in GRAPES 4DVar on Forecasts of Typhoon Shanshan