Estimating the Inversion Accuracy of Atmospheric Temperature and Water Vapor Profile Under Limb Sounding

Zong Xuemei

doi:10.11898/1001-7313.20200409

Vol.31, NO.4, 2020

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Zong Xuemei. Estimating the inversion accuracy of atmospheric temperature and water vapor profile under limb sounding. J Appl Meteor Sci, 2020, 31(4): 471-481. DOI: 10.11898/1001-7313.20200409.

Citation:

Zong Xuemei. Estimating the inversion accuracy of atmospheric temperature and water vapor profile under limb sounding. J Appl Meteor Sci, 2020, 31(4): 471-481. DOI: 10.11898/1001-7313.20200409.

Zong Xuemei. Estimating the inversion accuracy of atmospheric temperature and water vapor profile under limb sounding. J Appl Meteor Sci, 2020, 31(4): 471-481. DOI: 10.11898/1001-7313.20200409.

Citation:

Zong Xuemei. Estimating the inversion accuracy of atmospheric temperature and water vapor profile under limb sounding. J Appl Meteor Sci, 2020, 31(4): 471-481. DOI: 10.11898/1001-7313.20200409.

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Estimating the Inversion Accuracy of Atmospheric Temperature and Water Vapor Profile Under Limb Sounding

DOI: 10.11898/1001-7313.20200409

Zong Xuemei^,

Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029

Received Date: 2020-02-10
Rev Recd Date: 2020-03-31
Publish Date: 2020-07-31

Abstract

Abstract

Profiles of atmospheric temperature and water vapor are important for studying atmospheric state and play an important role in the energy balance of earth-atmosphere system. Limb remote sensing is an important means to obtain the profile of atmospheric parameters. The atmospheric radiation ultra-high spectral detector developed by Shanghai Institute of Technical Physics, Chinese Academy of Sciences, has a detection band range of 650-3050 cm^-1 and the spectral resolution on limb view is as high as 0.015 cm^-1, which will be the highest spectral resolution that the world's Fourier spectral detector can achieve. A method by using information and weighting function linearization are proposed to evaluate the inversion accuracy of the research instrument in advance. Weighting functions of atmospheric temperature and water vapor at 16 different tangent points are simulated and calculated by RFM model. The degree of signal freedom and the entropy reduction are also calculated by the information content method, and the optimal number of inversion channels is determined to be 200 by the stepwise iterative algorithm. Combined with the threshold (0.3 K) of detectable brightness temperature and the linearized weighting function of the instrument, the available spectral channel numbers of atmospheric temperature and volume mixing ratio of water vapor profiles under different inversion accuracy of six atmosphere models (US standard atmosphere, tropical atmosphere, middle-latitude summer atmosphere, middle-latitude winter atmosphere, subarctic summer atmosphere, subarctic winter atmosphere) are calculated and analyzed, and the inversion accuracy is estimated theoretically. On the demanded optimal 200 channels, the inversion accuracy of the whole temperature profile is 0.6 K, but if the inversion accuracy of the temperature profile is required to be 0.5 K, the number of channels available for inversion at a higher tangent height is smaller. Except the tropical atmosphere model, there are enough channels for the other five atmosphere models meeting 5% accuracy demands of the inversion of water vapor volume mixing ratio profiles. However, the inversion of the water vapor profile of the tropical atmosphere has barely enough channels at 16-20 km for 10% relative inversion accuracy of volume mixing ratio. The number of channels usable for atmospheric parameters retrieving increases by the decreasing of inversion accuracy. Among six atmosphere models, the tropical atmosphere is relatively special and its inversion accuracy is lower, which may be related to the unique temperature profile of the tropical atmosphere. There is no isothermal layer in the tropical atmosphere, which may lead to fewer atmospheric parameter inversion channels near the height of sharp temperature transition.
- limb sounding;
- weight function;
- information content;
- inversion accuracy

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References(32)

Figures and Tables

Fig. 1 Weighting function of atmospheric temperature at different tangent heights

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Fig. 2 Linear diagram(a) and logarithmic diagram(b) of CO₂'s line intensity(I)

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Fig. 3 Weighting function of water vapor at different tangent heights

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Fig. 4 Linear diagram(a) and logarithmic diagram(b) of water vapor's line intensity(I′)

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Fig. 5 Degree of signal freedom and entropy reduction of atmospheric temperature change with spectral channel numbers

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Fig. 6 Six atmospheric temperature profiles

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Fig. 7 Six volume mixing ratio profiles of water vapor

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Table 1 The sum of degree of signal freedom and entropy reduction at tangent heights of 11.0 km and 25.3 km for atmospheric temperature and water vapor for different channel numbers

光谱通道数	温度(11.5 km)		水汽(11.5 km)		温度(25.3 km)		水汽(25.3 km)
光谱通道数	信号自由度	熵减少量	信号自由度	熵减少量	信号自由度	熵减少量	信号自由度	熵减少量
5	2.194	11.07	2.179	4.89	2.260	10.74	2.116	4.23
10	2.789	11.53	2.765	5.34	2.887	11.23	2.719	4.69
20	3.405	11.98	3.340	5.77	3.510	11.69	3.353	5.16
50	4.221	12.58	4.077	6.31	4.330	12.29	4.217	5.80
100	4.840	13.03	4.635	6.71	4.949	12.74	4.877	6.28
200	5.460	13.48	5.227	7.14	5.549	13.18	5.533	6.75
300	5.825	13.74	5.581	7.40	5.882	13.42	5.921	7.03
400	6.085	13.93	5.837	7.58	6.111	13.58	6.179	7.22
500	6.288	14.08	6.038	7.73	6.288	13.71	6.383	7.37

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Table 2 Available channel numbers under six different atmospheric temperature inversion accuracy conditions

大气种类	精度/K	切点高度
大气种类	精度/K	0.0 km	4.6 km	11.5 km	16.1 km	20.7 km	25.3 km	34.5 km
	0.4	0	855	341	39	8	8	0
美国标准大气	0.5	3822	20346	8079	404	242	199	40
	0.6	8340	38723	22157	3353	1451	988	244
	0.4	0	328	6542	0	0	0	1
热带大气	0.5	150	17435	36485	9	19	88	72
	0.6	1463	36617	67314	142	369	474	582
	0.4	0	437	5052	8	2	0	0
中纬度夏季大气	0.5	601	17623	20875	254	173	115	57
	0.6	2717	36653	50575	2417	1189	571	397
	0.4	0	2308	134	98	43	16	0
中纬度冬季大气	0.5	2005	24629	1448	789	502	235	33
	0.6	9562	43214	16853	7924	5075	1349	201
	0.4	0	769	69	39	10	0	0
副北极夏季大气	0.5	1936	21870	1503	481	261	126	41
	0.6	5587	39388	11463	4279	2036	731	276
	0.4	0	4155	106	116	79	25	0
副北极冬季大气	0.5	43	28853	1257	1019	822	250	65
	0.6	8458	47559	13159	9734	7042	1407	262

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Table 3 Available channel numbers under six different volume mixing ratio of water vapor inversion accuracy conditions

大气种类	精度/%	切点高度
大气种类	精度/%	0.0 km	4.6 km	11.5 km	16.1 km	20.7 km	25.3 km	34.5 km
美国标准大气	5	321	19751	8897	1548	1155	2682	1403
美国标准大气	10	1785	30326	17579	14124	12749	12148	8797
热带大气	5	0	18484	13144	0	0	785	1791
热带大气	10	0	27393	21039	4108	6957	9057	9279
中纬度夏季大气	5	0	19728	12208	535	691	2081	2204
中纬度夏季大气	10	25	28958	19973	11114	11433	11615	10160
中纬度冬季大气	5	494	17679	5445	2451	2579	2686	887
中纬度冬季大气	10	2900	29436	15702	16700	14937	12417	7803
副北极夏季大气	5	42	20212	6505	2042	2759	3285	2235
副北极夏季大气	10	550	29661	15914	15428	15595	13479	10273
副北极冬季大气	5	3873	14174	5223	2519	3092	2206	918
副北极冬季大气	10	13723	26523	15956	17351	15782	12326	8048

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