Experiments on Improving Temperature and Humidity Profile Retrieval for Ground-based Microwave Radiometer

Zhang Xuefen; Wang Zhicheng; Mao Jiajia; Wang Zhangwei; Zhang Dongming; Tao Fa

doi:10.11898/1001-7313.20200401

Vol.31, NO.4, 2020

Turn off MathJax

Article Contents

Article Navigation > Journal of Applied Meteorological Science > 2020 > 31(4): 385-396

Zhang Xuefen, Wang Zhicheng, Mao Jiajia, et al. Experiments on improving temperature and humidity profile retrieval for ground-based microwave radiometer. J Appl Meteor Sci, 2020, 31(4): 385-396. DOI: 10.11898/1001-7313.20200401.

Citation:

Zhang Xuefen, Wang Zhicheng, Mao Jiajia, et al. Experiments on improving temperature and humidity profile retrieval for ground-based microwave radiometer. J Appl Meteor Sci, 2020, 31(4): 385-396. DOI: 10.11898/1001-7313.20200401.

Citation:

PDF( 1412 KB)

Experiments on Improving Temperature and Humidity Profile Retrieval for Ground-based Microwave Radiometer

DOI: 10.11898/1001-7313.20200401

1.
Meteorological Observation Center of CMA, Beijing 100081
2.
Zhejiang Atmospheric Detection Technology Support Center, Hangzhou 310018

Received Date: 2020-01-07
Rev Recd Date: 2020-04-14
Publish Date: 2020-07-31

Abstract

Abstract

Ground-based microwave radiometer (MWR) has a crucial role in scientific researches, weather modification service and climate change studies. MWR adopts passive remote sensing technology which has smaller volume, lower power consumption. Ground-based microwave radiometer detects atmospheric temperature and humidity by receiving atmospheric microwave radiation, which can conduct 24-hour unattended, high-resolution observation. It can detect short-time variation of atmospheric elements. Many studies show that different seasons, different weather conditions, quality control algorithms, and changes in environments have certain effects on retrieval results of MWR. In the case of cloudy condition, the uncertainty of cloud absorption coefficient leads to the increase of retrieval error and incorrect data of MWR especially. In order to improve temperature and relative humidity detection capabilities of MWR, the experiment builds BP neural network algorithm with 6 years(from 2011 to 2016) sounding data. The experiment builds two types of retrieval methods because there are some differences of microwave radiation transfer between clear and cloudy samples. The test uses measured brightness temperature data (requiring correction) and cloud data of millimeter-wavelength cloud radar as model inputs and then uses sounding data to evaluate model outputs (temperature and relative humidity profiles) from 2017 to 2018.Results show that correlation coefficients between outputs of 4 models (clear sky sample temperature model, cloudy sample temperature model, clear sky sample relative humidity model and the cloudy sample relative humidity model) and sounding data are 0.99, 0.99, 0.80 and 0.78. Taking sounding profiles as reference, root mean square errors (RMSE) of retrieval results of 4 models are 2.3℃, 2.3℃, 9%, 16%. Comparing with the MWR original profiles, RMSEs of 4 models are reduced by 0.4℃, 0.3℃, 11% and 9%, accuracies are improved by about 30%, 28%, 64% and 45%. In particular, the deviation of temperature model and humidity model within ±2℃ and ±20% account for 68%, 70% and 95%, 78%, which are 7%, 5% and 27%, 23% higher than MWR original profiles. The bias correction of brightness temperature and the training retrieval model of distinguishing weather samples are helpful to improve the retrieval accuracy of MWR temperature and humidity profiles. The network model combined with cloud radar information has obviously better effects on the retrieval result under cloudy samples.Through these experiments, the quality control of brightness temperature, combination of active and passive retrieval algorithms are well improved. The combination of active and passive retrieval effectively improves the performance of MWR, which will lay a foundation for the development of the comprehensive observation system of atmospheric profiles.
- ground-based microwave radiometer;
- BP neural network;
- temperature and humidity profiles;
- cloud radar

FullText(HTML)

References(31)

Figures and Tables

图 1 实测与模拟亮温拟合

(a)晴天条件, 22.24 GHz通道, (b)晴天条件, 58.00 GHz通道, (c)云天条件, 22.24 GHz通道, (d)云天条件, 58.00 GHz通道

Fig. 1 Linear fitting of measured and simulated brightness temperatures

(a)channel of 22.24 GHz in clear sky samples, (b)channel of 58.00 GHz in clear sky samples, (c)channel of 22.24 GHz in cloudy samples, (d)channel of 58.00 GHz in cloudy samples

DownLoad: Full-Size Img PowerPoint

图 2 晴天温度模型反演结果及LV2产品与探空对比

Fig. 2 Comparison of temperature profiles of retrieval model, LV2 and sounding in clear sky samples

(a)case at 1915 BT 23 Feb 2018, (b)case at 0715 BT 27 Mar 2017, (c)mean bias from Jan 2017 to Mar 2018, (d)root mean square error from Jan 2017 to Mar 2018

DownLoad: Full-Size Img PowerPoint

图 3 云天温度模型反演结果及LV2产品与探空对比

Fig. 3 Comparison of temperature profiles of retrieval model, LV2 and sounding in cloudy samples

(a)case at 0715 BT 17 Jan 2017, (b)case at 0715 BT 31 Oct 2017, (c)mean bias from Jan 2017 to Mar 2018, (d)root mean square error from Jan 2017 to Mar 2018

DownLoad: Full-Size Img PowerPoint

图 4 晴天相对湿度模型反演结果、LV2产品廓线与探空对比

Fig. 4 Comparison of relative humidity profiles of retrieval model, LV2 and sounding in clear sky samples

(a)case at 0715 BT 27 Mar 2017, (b)case at 0715 BT 26 Apr 2017, (c)mean bias from Jan 2017 to Mar 2018, (d)root mean square error from Jan 2017 to Mar 2018

DownLoad: Full-Size Img PowerPoint

图 5 云天相对湿度模型反演结果、LV2产品与探空对比

Fig. 5 Comparison of relative humidity profiles of retrieval model, LV2 and sounding in cloudy samples

(a)low cloud samples at 1915 BT 1 Feb 2018, (b)low cloud samples at 1315 BT 1 Aug 2017, (c)medium cloud samples at 0715 BT 8 Aug 2017, (d)medium cloud samples at 0715 BT 17 Jan 2018, (e)high cloud samples at 0715 BT 14 Dec 2017, (f)high cloud samples at 1915 BT 24 Jan 2017

DownLoad: Full-Size Img PowerPoint

图 6 云天相对湿度模型反演结果及LV2产品偏差廓线

(a)平均偏差廓线, (b)均方根误差廓线

Fig. 6 Relative humidity profiles of retrieval model and LV2 in cloudy samples

(a)mean bias, (b)root mean square error

DownLoad: Full-Size Img PowerPoint

Table 1 Relation between the simulated and the measured brightness temperatures

通道频点/GHz	订正关系式(晴天)	订正关系式(云天)
22.24	Y=1.0897X+0.1843	Y=1.0081X+1.8127
23.04	Y=1.0899X+0.3553	Y=1.0068X+1.8865
23.84	Y=1.0942X+0.0848	Y=1.0062X+1.5239
25.44	Y=1.1114X-0.4975	Y=1.0001X+1.0236
26.24	Y=1.1227X-0.4410	Y=0.9944X+1.2145
27.84	Y=1.1281X-0.5029	Y=0.9734X+1.3946
31.40	Y=1.1299X-0.5855	Y=0.9250X+2.1566
51.26	Y=1.2011X-16.9122	Y=0.8558X+18.4323
52.28	Y=1.2191X-28.6074	Y=0.9048X+16.7461
53.86	Y=1.0996X-21.2750	Y=1.0418X-7.2022
54.94	Y=1.0239X-6.5648	Y=1.0161X-4.3417
56.66	Y=0.9989X-0.3136	Y=0.9971X+0.2349
57.30	Y=0.9970X+0.2410	Y=0.9945X+0.9747
58.00	Y=0.9969X+0.3437	Y=0.9939X+1.2098

DownLoad: Download CSV

Table 2 Retrieval models and test schemes

方案	模型	是否进行亮温订正	是否考虑云的影响	反演结果
S1	晴天温度	否	否	SP1
S2	晴天温度	是	否	SP2
S3	云天温度	否	是	SP3
S4	云天温度	是	是	SP4
S5	晴天相对湿度	否	否	SP5
S6	晴天相对湿度	是	否	SP6
S7	云天相对湿度	否	是	SP7
S8	云天相对湿度	是	是	SP8

DownLoad: Download CSV

Table 3 Correlation and bias of LV2, SP1, SP2 to sounding in clear sky samples

高度/km	对比项	相关系数	均方根误差	不同偏差范围占比/%			准确性提升率/%
高度/km	对比项	相关系数	均方根误差	A	B	C	准确性提升率/%
	LV2产品	0.9956	1.5	56	30	14
[0, 2.5]	SP1	0.9903	2.0	47	28	25
	SP2	0.9937	1.5	56	28	16	6.78
	LV2产品	0.9852	3.2	17	20	63
(2.5, 4.5]	SP1	0.9839	4.3	8	13	79
	SP2	0.9879	2.2	34	30	36	42.08
	LV2产品	0.9821	3.1	26	25	49
(4.5, 10]	SP1	0.9810	4.3	14	15	71
	SP2	0.9837	2.8	32	25	43	19.17
	LV2产品	0.9954	2.7	35	26	39
[0, 10]	SP1	0.9913	3.6	25	19	56
	SP2	0.9946	2.3	41	27	32	29.98
注：误差占比范围A: [-1, 1], B:[-2, -1)∪(1, 2], C: (-∞, -2)∪(2, +∞), 单位:℃。表中仅统计SP2相对于LV2产品的提升率。

DownLoad: Download CSV

Table 4 Correlation and bias of LV2, SP3, SP4 to sounding in cloudy samples

高度/km	对比项	相关系数	均方根误差	不同偏差范围占比/%			准确性提升率/%
高度/km	对比项	相关系数	均方根误差	A	B	C	准确性提升率/%
	LV2产品	0.9953	1.4	61	27	12
[0, 2.5]	SP3	0.9812	2.4	37	36	27
	SP4	0.9944	1.3	67	22	11	30.93
	LV2产品	0.9873	2.5	31	25	44
(2.5, 4.5]	SP3	0.9795	9.0	0	0	100
	SP4	0.9829	2.0	40	31	29	35.29
	LV2产品	0.9838	3.2	38	31	31
(4.5, 10]	SP3	0.9807	11.6	0	1	35
	SP4	0.9822	2.9	42	35	23	23.15
	LV2产品	0.9953	2.6	41	24	35
[0, 10]	SP3	0.9675	8.9	14	14	72
	SP4	0.9946	2.3	45	25	30	27.76
注：误差占比范围A: [-1, 1], B:[-2, -1)∪(1, 2], C: (-∞, -2)∪(2, +∞), 单位:℃。表中仅统计SP4相对于LV2产品的提升率。

DownLoad: Download CSV

Table 5 Correlation and bias of LV2, SP5, SP6 to sounding in clear sky samples

高度/km	对比项	相关系数	均方根误差	不同偏差范围占比/%			准确性提升率/%
高度/km	对比项	相关系数	均方根误差	A	B	C	准确性提升率/%
	LV2产品	0.8246	12	60	30	10
[0, 2.5]	SP5	0.8349	11	71	21	8
	SP6	0.8529	10	77	17	6	27.13
	LV2产品	0.6880	17	42	35	23
(2.5, 4.5]	SP5	0.6575	13	68	19	13
	SP6	0.7059	10	73	19	8	50.42
	LV2产品	0.3073	25	19	29	52
(4.5, 10]	SP5	0.2426	9	87	9	4
	SP6	0.3576	8	88	9	3	74.02
	LV2产品	0.5800	20	38	30	32
[0, 10]	SP5	0.7606	10	78	15	7
	SP6	0.8006	9	81	14	5	64.28
注：误差占比范围A: [-10, 10], B:[-20, -10)∪(10, 20], C: (-∞, -20)∪(20, +∞), 单位:%。表中仅统计SP6相对于LV2产品的提升率。

DownLoad: Download CSV

Table 6 Correlation of LV2, SP7, SP8 to sounding in cloudy samples with the bias

高度/km	对比项	相关系数	均方根误差	不同偏差范围占比/%			准确性提升率/%
高度/km	对比项	相关系数	均方根误差	A	B	C	准确性提升率/%
	LV2产品	0.7599	19	37	32	31
[0, 2.5]	SP7	0.8143	15	59	23	18
	SP8	0.8439	14	63	23	14	35.50
	LV2产品	0.6077	26	26	29	45
(2.5, 4.5]	SP7	0.7417	28	33	20	47
	SP8	0.7770	18	46	29	25	46.27
	LV2产品	0.3145	28	20	23	57
(4.5, 10]	SP7	0.6302	24	39	25	36
	SP8	0.7477	18	45	29	26	48.91
	LV2产品	0.5478	25	23	27	45
[0, 10]	SP7	0.6764	22	45	24	31
	SP8	0.7855	16	51	27	22	44.95
注：误差占比范围A: [-10, 10], B:[-20, -10)∪(10, 20], C: (-∞, -20)∪(20, +∞), 单位：%。表中仅统计SP8相对于LV2产品的提升率。

DownLoad: Download CSV

References

[1]	孙学金.大气探测学.北京:气象出版社, 2009.
[2]	周秀骥.中尺度气象学研究与中国气象科学研究院.应用气象学报, 2006, 17(6):665-671. http://qikan.camscma.cn/jamsweb/article/id/200606115
[3]	陈明轩, 俞小鼎, 谭晓光, 等.对流天气临近预报技术的发展与研究进展.应用气象学报, 2004, 15(6):754-766. http://qikan.camscma.cn/jamsweb/article/id/20040693
[4]	郭丽君, 郭学良.利用地基多通道微波辐射计遥感反演华北持续性大雾天气温、湿度廓线的检验研究.气象学报, 2015, 73(2):368-381. http://d.old.wanfangdata.com.cn/Periodical/qxxb201502012
[5]	Raju C S, Renju R, Antony T, et al.Microwave radiometric observation of a waterspout over coastal Arabian Sea.IEEE Geoscience & Remote Sensing Letters, 2013, 10(5):1075-1079. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=343856de9be826fc5f33977ee84a84f9
[6]	Ware R, Carpenter R, Güldner J, et al.A multichannel radiometric profiler of temperature, humidity, and cloud liquid.Radio Science, 2016, 38(4):44-1-44-13. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=91eb3b202eb0ec2c092c73ca8f032e81
[7]	鲍艳松, 钱程, 闵锦忠, 等.利用地基微波辐射计资料反演0~10 km大气温湿廓线试验研究.热带气象学报, 2016, 32(2):163-171. http://d.old.wanfangdata.com.cn/Periodical/rdqxxb201602003
[8]	刘亚亚, 毛节泰, 刘钧, 等.地基微波辐射计遥感大气廓线的BP神经网络反演方法研究.高原气象, 2010, 29(6):1514-1523. http://d.old.wanfangdata.com.cn/Conference/7570338
[9]	王云, 王振会, 李青, 等.基于一维变分算法的地基微波辐射计遥感大气温湿廓线研究.气象学报, 2014, 72(3):570-582. http://d.old.wanfangdata.com.cn/Periodical/qxxb201403011
[10]	刘思波, 何文英, 刘红燕, 等.地基微波辐射计探测大气边界层高度方法.应用气象学报, 2015, 26(5):626-635. doi: 10.11898/1001-7313.20150512
[11]	段英, 吴志会.利用地基遥感方法监测大气中汽态、液态水含量分布特征的分析.应用气象学报, 1999, 10(1):34-40. http://qikan.camscma.cn/jamsweb/article/id/19990133
[12]	郝巨飞, 袁雷武, 李芷霞, 等.激光雷达和微波辐射计对邢台市一次沙尘天气的探测分析.高原气象, 2018, 37(4):1110-1119. http://d.old.wanfangdata.com.cn/Periodical/gyqx201804023
[13]	刘建忠.不同时次地基微波辐射计反演产品评估.气象科技, 2012, 40(3):332-339. http://d.old.wanfangdata.com.cn/Periodical/qxkj201203002
[14]	刘红燕.三年地基微波辐射计探测温度廓线的精度分析.气象学报, 2011, 69(4):719-728.
[15]	侯叶叶.地基微波辐射计反演水汽密度廓线精度分析.气象科技, 2016, 44(5):702-709. http://d.old.wanfangdata.com.cn/Periodical/qxkj201605002
[16]	张文刚, 徐桂荣, 颜国跑, 等.微波辐射计与探空仪测值对比分析.气象科技, 2014, 42(5):737-741. http://d.old.wanfangdata.com.cn/Periodical/qxkj201405002
[17]	魏重, 雷恒池, 沈志来.地基微波辐射计的雨天探测.应用气象学报, 2001, 12(增刊I):65-72. http://d.old.wanfangdata.com.cn/Periodical/yyqxxb2001z1009
[18]	Liljegren J C, Clothiaux E E, Mace G G, et al.A new retrieval for cloud liquid water path using a ground-based microwave radiometer and measurements of cloud temperature.J Geophys Res Atmos, 2001, 106(D13):14485-14500. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=191a8b41836bfa2fdf7f1fe28ca194b0
[19]	Frate F D, Schiavon G.A combined natural orthogonal functions/neural network technique for the radiometric estimation of atmospheric profiles.Radio Science, 1998, 33(2):405-410.
[20]	车云飞, 马舒庆, 杨玲, 等.云对地基微波辐射计反演湿度廓线的影响.应用气象学报, 2015, 26(2):193-202. doi: 10.11898/1001-7313.20150207
[21]	茆佳佳, 张雪芬, 王志诚, 等.多型号地基微波辐射计亮温准确性比对.应用气象学报, 2018, 29(6):86-98. doi: 10.11898/1001-7313.20180608
[22]	王志诚, 张雪芬, 茆佳佳, 等.不同天气条件下地基微波辐射计探测性能比对.应用气象学报, 2018, 29(3):282-295. doi: 10.11898/1001-7313.20180303
[23]	唐英杰, 马舒庆, 杨玲, 等.云底高度的地基毫米波云雷达观测及其对比.应用气象学报, 2015, 26(6):680-687. doi: 10.11898/1001-7313.20150604
[24]	袁野, 朱士超, 李爱华.黄山雨滴下落过程滴谱变化特征.应用气象学报, 2016, 27(6):734-740. doi: 10.11898/1001-7313.20160610
[25]	陶法, 胡树贞, 张雪芬.地基可见光/红外全天空成像仪数据融合.气象, 2018, 44(4):52-59. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=qx201804005
[26]	周毓荃, 欧建军.利用探空数据分析云垂直结构的方法及其应用研究.气象, 2010, 36(11):50-58. http://d.old.wanfangdata.com.cn/Periodical/qx201011008
[27]	Clough S A, Shephard M W, Mlawer E J.Atmospheric radiative transfer modeling:A summary of the AER codes.Journal of Quantitative Spectroscopy & Radiative Transfer, 2005, 91:233-244. doi: 10.1016-j.jqsrt.2004.05.058/
[28]	Pierdicca N, Pulvirenti L, Marzano F S.A model to predict cloud density from midlatitude atmospheric soundings for microwave radiative transfer applications.Radio Science, 2016, 41(6):1-12. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=cb8a7699ce88e212ef2279d8279fe9e4
[29]	顾成明, 王云峰, 张晓辉, 等.云参数对微波亮温模拟计算的影响试验.应用气象学报, 2016, 27(3):380-384. doi: 10.11898/1001-7313.20160313
[30]	傅新姝, 谈建国.地基微波辐射计探测资料质量控制方法.应用气象学报, 2017, 28(2):209-217. doi: 10.11898/1001-7313.20170208
[31]	气象观测专用技术装备测试方法地面气象观测设备(试行).北京: 中国气象局, 2015.