661 Estimating GPS RO Bias in Cloudy Conditions and Its Dependence on Cloud Fraction Along GPS RO Limb Tracks

Tuesday, 24 January 2017
4E (Washington State Convention Center )
Shengpeng Yang Sr., Nanjing University of Information & Science Techology, Nanjing, China; and X. Zou

An important quantity that must be properly estimated for GPS RO data assimilation in numerical weather prediction is the GPS RO data bias, which must be removed from data before these data be assimilated into NWP data assimilation systems. When compared with the European Centre for Medium-Range Weather Forecasts (ECMWF) analyses, atmospheric refractivity measurements from Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) Global Positioning System (GPS) radio occultation (RO) mission were found to be positively biased in cloudy conditions, and those in clear-sky conditions were not biased (Lin et al. 2010; Zou et al. 2012; Yang and Zou 2012). Given the fact that a GPS refractivity measurement represents an integrated effect of the atmosphere along its limb track over a distance of a few hundred kilometers centered at the tangent point on the propagation bending of the two GPS emitted radio signals, this study investigates the dependent of the GPS RO data biases on cloud scales along GPS RO’s limb tracks using the global COSMIC RO data and collocated NOAA-18 Advanced Microwave Sounding Unit-A (AMSU-A) data and CloudSat Cloud Profiling Radar (CPR) data over a two year period. Specifically, the collocated AMSU-A liquid water path (LWP) retrieval over the ocean is used to quantify the dependence of fractional refractivity bias (N-bias) of GPS RO profiles in cloudy conditions on the number of AMSU-A data points with non-zero LWP along GPS RO limb tracks. The collocated CPR cloud types used for selecting GPS RO profiles for grouping GPS RO cloudy profiles into seven different cloud types. It is shown that the positive fractional N-bias varies with the cloud fraction along the COSMIC GPS RO limb tracks. It reaches a value between 1-2% when cloud fraction is as high as 90-100% for altocumulus, altostratus, cirrus, cumulus and deep convection clouds. For nimbostratus and stratocumulus clouds, large biases are found at any values of cloud fraction. The positive fractional N-bias can be more than 2% even if cloud fraction is less than about 50% for nimbostratus and stratus clouds.
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