Met Office, Exeter EX1 3PB, UK Manuscript received: 2021-02-22 Manuscript revised: 2021-05-10 Manuscript accepted: 2021-05-24 Abstract:Microwave radiances from passive polar-orbiting radiometers have been, until recently, assimilated in the Met Office global numerical weather prediction system after the scenes significantly affected by atmospheric scattering are discarded. Recent system upgrades have seen the introduction of a scattering-permitting observation operator and the development of a variable observation error using both liquid and ice water paths as proxies of scattering-induced bias. Applied to the Fengyun 3 Microwave Temperature Sounder 2 (MWTS-2) and the Microwave Humidity Sounder 2 (MWHS-2), this methodology increases the data usage by up to 8% at 183 GHz. It also allows for the investigation into the assimilation of MWHS-2 118 GHz channels, sensitive to temperature and lower tropospheric humidity, but whose large sensitivity to ice cloud have prevented their use thus far. While the impact on the forecast is mostly neutral with small but significant short-range improvements, 0.3% in terms of root mean square error, for southern winds and low-level temperature, balanced by 0.2% degradations of short-range northern and tropical low-level temperature, benefits are observed in the background fit of independent instruments used in the system. The lower tropospheric temperature sounding Infrared Atmospheric Sounding Interferometer (IASI) channels see a reduction of the standard deviation in the background departure of up to 1.2%. The Advanced Microwave Sounding Unit A (AMSU-A) stratospheric sounding channels improve by up to 0.5% and the Microwave Humidity Sounder (MHS) humidity sounding channels improve by up to 0.4%. Keywords: microwave remote sensing, numerical weather prediction, data assimilation, Fengyun 3 摘要:大气订正后的极轨卫星微波辐射计数据不久前同化进入英国气象局全球数值天气预报系统。最新的系统升级已经引入一个允许散射的观测算子,同时使用液态水和冰水路径作为散射诱导的代理,产生了可变的观测误差。这种方法应用到风云三号气象卫星微波温度计2(MWTS-2)和微波湿度计2(MWHS-2)后,将183GHz通道数据的可用性提高了最多8%。同时这种方法的引入还实现了MWHS-2 118GHz通道数据的同化研究,该通道对温度和对流层低层湿度敏感,但该通道对冰云的高度敏感影响了对该通道的应用。通过验证表明,总体来看对预报的影响是中性的,南半球风场和低层气温短期预报改善幅度很小但很显著,均方根误差为0.3%,但对北半球和热带地区会下降0.2%。对短期预报的详细检查表明,微波和红外光谱域对背景的观测拟合得到了改进。对红外大气探测干涉仪(IASI)各通道对流层低层大气温度背景偏差的标准差的改进达到1.2%。对先进微波探测仪A(AMSU-A)平流层探测通道的改进达到0.5%,对微波湿度计(MHS)湿度探测通道的改进达到0.4%。 关键词:微波遥感, 数值天气预报, 数值同化, 风云三号
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4. Conclusions Recent system developments have led, for the first time, to the all-sky assimilation of microwave radiances in the Met Office global model. The implementation of the scattering-permitting fast radiative transfer model RTTOV-SCATT along with the improved partitioning of the total water amount in 1D-Var has made it possible to use the retrieved liquid and ice water path as proxies to inflate the observation error of microwave radiance affected by clouds. A strategy that has been successfully applied to radiances from the AMSU-A and MHS instruments, which are now assimilated operationally in all-sky (non-precipitating) conditions in the Met Office global model. The microwave temperature and humidity sounders onboard the Chinese platforms of the FY-3 series, presenting characteristics close to the ATOVS systems plus a unique set of channels sounding the 118 GHz oxygen band, have become the next logical candidate for the all-sky radiance assimilation. In addition to the potential benefit from a more aggressive use of available observations, insights into the added value of the 118 GHz channels can guide the applications related to future satellite missions such as the Microwave Imager (MWI) onboard MetOp-SG platforms or the Cubesats constellation TROPICS (Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats) supported by the NASA Earth Venture-Instrument (EVI-3) program. Several set-ups have been tested in which radiances from MWTS-2 and MWHS-2 were assimilated in all-sky (except precipitating scenes). The configuration in which MWHS-2 high peaking 118.75 ± 0.08, ±0.2 and ±0.3 GHz, and 183.31 ± 1 and ±1.8 GHz channels are assimilated with a fixed observation error while the low peaking 118.75 ± 0.8, ±1.1 and ±2.5 GHz, and 183.31 ± 3.0, ±4.5 and ±7.0 GHz are assimilated with a variable observation error has yielded the best impact. The variable observation errors for these channels vary with the retrieved values of LWP and IWP derived from the Met Office 1D-Var observation processing system. The verification against ECMWF analyses for key atmospheric variables at different forecast lead times highlights small but significant short-range improvements regarding southern hemispheric winds and low-level temperature balanced by some degradation of short-range northern and tropical low-level temperatures. The overall impact is neutral. A detailed examination of the short-range forecasts shows an improvement of the observation fit to the background across the microwave and the infrared spectral domains. The use of the all-sky assimilation methodology for MWHS-2 183 GHz channels alone yields up to 0.4% improvement for IASI lower tropospheric humidity and temperature sounding channels, and up to 0.3% improvement for both AMSU-A stratospheric temperature sounding channels and MHS humidity sounding channels. The MWHS-2 highest humidity sounding channels 183.31 ± 1.0 and 183.31 ± 1.8 GHz, however, does not add significant benefit when used in all-sky (while increasing the computational cost through the use of RTTOV-SCATT) for the configuration tested in this study. The relaxing of the bennartzrain scattering test may let more cloudy radiances into the assimilation system and drive further benefits. This will be subject to investigation for a future model upgrade. The additional assimilation of five 118 GHz channels (118.75 ± 0.08, ±0.2, ±0.3, ±0.8, ±1.1, and ± 2.5 GHz), including three in all-sky (118.75 ± 0.8, ±1.1, and ±2.5 GHz) further improves the fit to the background of most instruments assimilated in the system. The improvement reaches 1.2% for IASI low peaking temperature sounding channels and 0.5% for AMSU-A stratospheric temperature sounding channels. The combination of temperature and humidity sensitivity of the 118.75 ± 2.5 GHz appears particularly effective in improving the IASI fit to the background in the 773–811 cm?1 and 1096–1204 cm?1 spectral ranges. Pending further verifications, such as high-resolution assimilation experiments, the all-sky assimilation of MWHS-2 radiances at 118 and 183 GHz is a candidate for future implementation in the Met Office system.
Channel
Weighting function peaking pressure (hPa)
Frequency (GHz)
Usage
MWTS-2
1
1050
50.30 QH
Used for gross error checks only
2
1050
51.76 QH
Used for gross error checks only
3
962
52.80 QH
Used for gross error checks only
4
661
53.596 ± 0.115 QH
Rejected when mwbcloudy and bennartzrain Rejected over sea ice and land Used in 1D-Var only
5
410
54.40 QH
Rejected when bennartzrain Rejected over sea ice and highland (and land in the tropics) Used in 1D-Var only
6
300
54.94 QH
Rejected when bennartzrain Rejected over sea ice and highland Used in 1D-Var only
7
181
55.50 QH
Rejected when bennartzrain Rejected over sea ice and highland Used in 1D-Var only
8
97
57.29 QH
Used in 1D-Var only
9
55
57.29 ± 0.217 QH
Used in 1D-Var and 4D-Var
10
29
57.29 ± 0.3222 ± 0.048 QH
Used in 1D-Var and 4D-Var
11
10
57.29 ± 0.3222 ± 0.022 QH
Used in 1D-Var and 4D-Var
12
4
57.29 ± 0.3222 ± 0.010 QH
Used in 1D-Var and 4D-Var
13
2
57.29 ± 0.3222 ± 0.0045 QH
Used in 1D-Var and 4D-Var
MWHS-2
1
1050
89.0 QH
Used for gross error checks only
2
25
118.75 ± 0.08 QV
Not used
3
55
118.75 ± 0.2 QV
Not used
4
97
118.75 ± 0.3 QV
Not used
5
236
118.75 ± 0.8 QV
Not used
6
372
118.75 ± 1.1 QV
Not used
7
1033
118.75 ± 2.5 QV
Not used
8
1033
118.75 ± 3.0 QV
Not used
9
1050
118.75 ± 5.0 QV
Not used
10
1033
150 QH
Used for gross error checks only
11
491
183.31 ± 1 QV
Rejected when bennartzrain Rejected over sea ice and highland Rejected when surface to space transmittance > 0.15 Used in 1D-Var and 4D-Var
12
533
183.31 ± 1.8 QV
Rejected when bennartzrain Rejected over sea ice and highland Rejected when surface to space transmittance > 0.15 Used in 1D-Var and 4D-Var
13
618
183.31 ± 3.0 QV
Rejected when mwbcloudy and bennartzrain Rejected over sea ice and highland Rejected when surface to space transmittance > 0.15 Used in 1D-Var and 4D-Var
14
704
183.31 ± 4.5 QV
Rejected when mwbcloudy and bennartzrain Rejected over sea ice and highland Rejected when surface to space transmittance > 0.15 Used in 1D-Var and 4D-Var
15
826
183.31 ± 7.0 QV
Rejected when mwbcloudy and bennartzrain Rejected over sea ice and land Rejected when surface to space transmittance > 0.15 Used in 1D-Var and 4D-Var
Table1. Summary of MWTS-2 and MWHS-2 channel usage and rejection criteria. Weighting function peaking pressure has been calculated with RTTOV 54-level coefficients, at nadir, for the U.S. standard atmosphere, and rounded to the nearest hPa.
Figure1. FY-3D MWHS-2 183 ± 1 GHz background departures (K) on 1200 UTC 8 April 2020. (a) panel shows all valid data including scattering scenes, (b) panel shows the data where LWP is less than 0.05 kg m?2 and IWP greater than 0.1 kg m?2, i.e. mainly of ice cloud, and (c) panel shows the data where LWP is greater than 0.05 kg m?2 and IWP less than 0.1 kg m?2, i.e. mainly liquid cloud.
Figure2. Observation error standard deviation for (a) MWTS-2 57.29 ± 0.217 GHz, (b) MWHS-2 118.75 ± 2.5 GHz, (c) MWHS-2 183.31 ± 1 GHz, and (d) MWHS-2 183.31 ± 7 GHz as a function of LWP and IWP. The colored contour shows the standard deviation in the background departure and the blue mesh shows the fit from the least square regression.
Channel number & frequency (GHz)
$ \sigma _i^{{\rm{clr}}} $ (K)
ai (±1σ) (K kg?1 m2)
bi (±1σ) (K kg?1 m2)
MWTS-2
9 (57.29±0.217)
0.7300
0
0.5306 (0.1438)
10 (57.29±0.217±0.048)
1.3100
0
0.4196 (0.1095)
11 (57.29±0.217±0.022)
1.3700
0
0.5023 (0.0741)
12 (57.29±0.217±0.010)
2.3200
0
0.3123 (0.0691)
13 (57.29±0.217±0.0045)
4.7900
0
0.1282 (0.0315)
MWHS-2
2 (118.75±0.08)
4.0000
0
0.3942 (0.0935)
3 (118.75±0.2)
3.0000
0
0.2762 (0.0496)
4 (118.75±0.3)
3.0000
0
0.1009 (0.0288)
5 (118.75±0.8)
4.0000
0
0.3959 (0.0581)
6 (118.75±1.1)
4.0000
0.0166 (0.0410)
0.5973 (0.0216)
7 (118.75±2.5)
4.0000
3.9740 (0.1506)
3.1724 (0.0875)
11 (183.31±1)
2.8028
2.8780 (0.3799)
0.0529 (0.0912)
12 (183.31±1.8)
2.6230
2.3057 (0.2904)
4.0106 (2.1055)
13 (183.31±3.0)
1.7717
1.9393 (0.2877)
0.8576 (0.1243)
14 (183.31±4.5)
1.9913
1.3075 (0.2939)
2.3607 (0.1585)
15 (183.31±7.0)
1.9981
0.2561 (0.3291)
5.4332 (0.2487)
Table2. Clear-sky observation error standard deviation and regression coefficients (with the coefficient 1σ standard error in brackets) for the MWTS-2 and MWHS-2 channels assimilated in VAR. The values in bold show the channels for which the least square regression fits reasonably well with the standard deviation in O-B from the training set.
RMSE (%) change against observations
RMSE (%) change against ECMWF analyses
EXP-1 vs CRTL
0.03
?0.01
EXP-2 vs CRTL
0.01
?0.05
EXP-3 vs CRTL
0.05
?0.03
EXP-4 vs CRTL
0.12
0.03
EXP-4 vs EXP-1
0.09
0.04
Table3. Summary of the overall RMSE (%) change against observations (left) and against ECMWF analyses (right) for EXP-1 to -4 vs. CRTL and for EXP-4 vs. EXP-1.
Figure3. Change in the root-mean-square forecast error between EXP-1 (a), EXP-4 (b), and the control for key atmospheric variables at lead times from T+6 to T+168 with respect to the ECMWF analyses. Triangle color, size, and direction are given by 100 x (control RMSE–trial RMSE) / control RMSE. Upward green indicates that the trial RMSE is smaller than the control RMSE. Downward purple indicates that the trial RMSE is larger than the control RMSE. Significance is given by shading.
Figure4. RMSE difference between EXP-4 and EXP-1 with respect to the ECMWF analyses.
Figure5. Change in standard deviation in the background departure for MetOp B IASI (a) and MetOp B ATOVS (b) in EXP-1. Red indicates a significant increase, green a significant decrease, and blue no significant change. The numbers at the top of each plot indicate the mean change across all channels (±1σ).
Acknowledgements. This work was supported by the UK – China Research & Innovation Partnership Fund through the Met Office Climate Science for Service Partnership (CSSP) China as part of the Newton Fund. We are grateful to Chawn HARLOW for the useful discussions that helped us improve this study. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.