Microphysical retrieval from Dual-frequency GPM observations

ABSTRACT

Following the success of the Tropical Rainfall Measuring Mission (TRMM), considerable effort has been directed at the next generation of space-based precipitation radar (PR) to be launched aboard the Global Precipitation Measuring (GPM) core satellite. GPM is a science mission with integrated application goals for advancing the knowledge of the global water/energy cycle variability as well as improving weather, climate and hydrological prediction capabilities through more accurate and frequent precipitation measurements around the global every 2 to 4 h. The GPM mission concept is centered on the deployment of a Core Observatory satellite with an active dual-frequency precipitation radar (DPR) , operating at Ku and Ka bands and a passive GPM Microwave Imager (GMI). The dual-frequency precipitation radar (DPR) aboard the GPM core satellite is expected to improve our knowledge of precipitation processes relative to the single-frequency on microphysics, and better accuracies in rainfall and liquid water content retrievals. Overall, the combination of Ku and Ka bands should significantly improve the detection thresholds for light rain and snow relative to TRMM.

Many algorithms have been proposed to do retrieval of drop size distribution (DSD) and rain-rate estimation at each range bin along the vertical profile using combined Ku and Ka measured reflectivity. Generally, there are two main types of dual-frequency algorithm that can be used within a down-looking space radar. 1) the forward method, where the DSDs are calculated at each bin starting from the top bin and moving down to the bottom; and 2) the backward method, where the algorithm begins at the bottom bin and moves upward to the top. Forward methods have limited application because of a tendency to diverge in regions of moderate-to-heavy attenuation or moderate-to-heavy rainfall (Liao and Meneghini 2004). Backward algorithms tend to be more stable than the forward types but it requires an a priori knowledge of the total two-way path-integrated attenuation (PIA) for each ray or an ability to calculate it (Meneghini et al. 1997, 2002). Another retrieval algorithm used for GPM is an iterative, dual-frequency algorithm that does not use PIA derived from SRT but instead estimates it as part of an iterative process (Mardiana et al. 2004). Rose and Chandrasekar (2005) incorporated the dual-frequency iterative algorithm of Mardiana et al (2004) into a single-loop feedback-control structure and showed that about half of global rainfall could be incorrectly estimated. A supplementary method , using a linear model of vertical profiles for Do and log(Nw) for rain region was proposed by Rose and Chandrasekar (2006) through an iterative way. This method offers advantages over the single-loop model in that it is not susceptible to the bi-valued Do ambiguity for rain described in detail by Liao et al.(2003), Mardiana et al (2004), and Meneghini et al.(2002).

In this paper, an alternate approach combining the advantages of forward method and linear Do and log(Nw) model will be proposed to do DSD retrieval for the whole vertical profile including frozen, melting and rain part. Forward method is applied to frozen and melting part while linear Do and log(Nw) model is used for the rain part. This new approach uses iterative way to optimize Do and log(Nw) at bottom of rain region by constructing the cost function along the whole profile. When measurements from melting part changes substantially, an alternative approach based on (Yokoyama et al 1984) model will be used within melting layer instead of retrieving forward directly. The relation between water fraction and the distance to 0 degree isotherm within melting layer follows the model described in (Yokoyama et al 1984). The proposed algorithm is tested using Ku and Ka band vertical profiles simulated from NAMMA experiment in 2006. Retrieved DSDs from the new method will be compared with the simulated truth to evaluate the performance of the new approach. Rain-rate estimated from retrieved DSDs will also be compared with rain-rate calculated using “true DSDs”.