Characterization of Brightness Temperature Biases at Channels 13 and 14 for FY-3C MWHS-2

Hu, Juyang; Lu, Qifeng; Dong, Xiaolong; Wu, Chunqiang; Sun, Fenglin; Guo, Yang; Gu, Shanqiang; An, Duo; Wu, Shengli; Dou, Fangli

doi:10.1109/tgrs.2021.3126319

Cited by 2 publications

(2 citation statements)

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“…Gradient Boosting Decision Tree (GBDT) is considered to be one of the best performing ensemble learning methods in machine learning. GBDT uses the negative gradient of the loss function to fit the residual of the previous round of base learners, so that the residual estimate of each round gradually decreases close to the actual value [36]. GBDT improves the generalization ability and robustness of a single model and has an interpretable regression procedure.…”

Section: Methodology a Gradient Boosting Decision Tree (Gbdt)mentioning

confidence: 99%

“…Before the HATPRO moved to Bern, Hervo et al The feature importance calculation of gradient boosting decision tree (GBDT) can further quantify the sensitivity of multiple factors to the contribution of T b bias and thus infer the source of the bias [36]. The bias may be caused by a combination of factors, including voltages, channel gain (slope of the linear response), and temperature of ambient blackbody target, which are related to instrument calibration; environmental temperature and environmental relative humidity, which are related to the instrument surrounding environment; receiver temperature and receiver stability, which are related to receiver performance; sun elevation angle, which is related to the position of the sun relative to the instrument and diurnal distribution.…”

Section: A Brightness Temperature Comparisonmentioning

confidence: 99%

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An Indoor Microwave Radiometer for Measurement of Tropospheric Water

Wang

Murk

Sauvageat

et al. 2023

IEEE Trans. Geosci. Remote Sensing

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This paper presents the first detailed description of the innovative measurement set up of an indoor tropospheric microwave radiometer (TROWARA) that avoids water films on radome. We discuss the performance of a commercial outdoor microwave radiometer (HATPRO) for measuring tropospheric water parameters in Bern, Switzerland. The HATPRO is less than 20 meters from the TROWARA and has different instrument characteristics. Brightness temperatures measured by HATPRO are analyzed by comparing them with coincident measurements from TROWARA and radiative transfer simulations based on the ECMWF operational analysis data (denoted as RTSE). To find the source of brightness temperature bias, a gradient boosting decision tree is used to analyze the sensitivity of eight feature factors to bias. Data processing routines of the two radiometers use different algorithms to retrieve integrated water vapour (IWV) and integrated cloud liquid water (ILW), whereas the same physical algorithms based on the radiative transfer equation are applied to obtain the opacity and rain rate. Using 62 days of data with varied weather conditions, it was found that TROWARA brightness temperatures are in good agreement with RTSE. HATPRO brightness temperatures are significantly overestimated by about 5 K at 22 GHz, compared to TROWARA and RTSE. HATPRO brightness temperatures at 31 GHz agree well with TROWARA and RTSE (within about +/-1 K). The overestimated brightness temperatures in the K-band and the HATPRO retrieval algorithm lead to an overestimation of IWV and ILW by HATPRO. The opacities at 31 GHz match very well for TROWARA and HATPRO during no rain with a verified R 2 of 0.96. However, liquid water floating or remaining water films on the radome of the outdoor HATPRO radiometer induce an overestimation of the rain rate. The physical reason for the overestimated 22 GHz brightness temperatures of the HATPRO is mainly the result of the combined effect of instrument calibration, the surrounding environment of the instrument, and the sun elevation angle. This can be a problem with the Generation 2 HATPRO radiometer and this problem was resolved in the Generation 5 HATPRO radiometer.

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Section: Methodology a Gradient Boosting Decision Tree (Gbdt)mentioning

confidence: 99%

Section: A Brightness Temperature Comparisonmentioning

confidence: 99%