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Monitoring the performance of the Fengyun satellite instruments using radiative transfer models and NWP fields

Qifeng Lu, Juyang Hu, Chunqiang Wu, Chengli Qi, Shengli Wu, Na Xu, Ling Sun, Xiaoqing Li, Hui Liu, Yang Guo, Dawei An, Fenglin Sun

2020Journal of Quantitative Spectroscopy and Radiative Transfer23 citationsDOIOpen Access PDF

Abstract

Fengyun (FY) satellite program is becoming the important component of global earth observing system. Its instrumental performance, i.e., data quality and stability, play a critical role to support the quantitative applications, such as atmospheric and surface parameter retrievals, numerical weather prediction (NWP) and the production of Climate Data Records (CDRs). For monitoring the instrumental performance, the radiative transfer models (RTMs) together with NWP fields are used to simulate the reference radiance in FY satellite instrument monitoring schemes (FY-SIMS). Through monitoring the difference of observed and simulated brightness temperature of satellite instruments (OMB) against instrument parameters, the quality and stability can be evaluated from different viewpoint of geographic location, viewing geometry, time and spectral ranges. Two components of FY-SIMS, FY satellite data status monitoring system (FY-SDSMon) and satellite instrument characterization (FY-SIChar), are introduced. FY-SDSMon can provide near-real-time and long-time monitoring using fast RTMs and the China Meteorological Administration operational NWP fields, giving a routine and general knowledge of the instrument performance and its potential risk warning. FY-SIChar helps on further characterization of instruments by using more kinds of RTMs and comparison with counterpart instruments. Several examples show that FY-SIMS gives the insight to understand the instrument performance and characterize the potential bias. With the FY-SIMS, the performance of IR and MW band is widely monitored, and recently the VIS-NIR band is also in test to be monitored. Due to the improved quality and stability, the FY data is well evaluated and assimilated by worldwide NWP community to improve the forecast skill.

Topics & Concepts

RadianceRemote sensingSatelliteEnvironmental scienceRadiative transferMeteorologyNumerical weather predictionStability (learning theory)Computer sciencePhysicsGeographyOpticsMachine learningAstronomyMeteorological Phenomena and SimulationsAtmospheric aerosols and cloudsCalibration and Measurement Techniques