Comparison of TRMM and Water District Rain Rates over New Mexico

This paper compares monthly and seasonal rain rates derived from the Version 5 （V5） and Version 6 （V6） TRMM Precipitation Radar （TPR, TSDIS reference 2A25）, TRMM Microwave Imager （TMI, 2A12）, TRMM Combined Instrument （TCI, 2B31）, TRMM calibrated IR rain estimates （3B42） and TRMM merged gauge and satellite analysis （3B43） algorithms over New Mexico （NM） with rain gauge analyses provided by the New Mexico water districts （WD）. The average rain rates over the NM region for 1998- 2002 are 0.91 mm d^-1 for WD and 0.75, 1.38, 1.49, 1.27, and 1.07 mm d^-1 for V5 3B43, 3B42, TMI, PR and TCA; and 0.74, 1.38, 0.87 and 0.97 mm d^-1 for V6 3B43, TMI, TPR and TCA, respectively. Comparison of V5 3B43 with WD rain rates and the daily TRMM mission index （TPR and TMI） suggests that the low bias of V5 3B43 for the wet months （summer to early fall） may be due to the non-inclusion of some rain events in the operational gauge analyses that. are used in the production of V5 3B43. Correlation analyses show that the WD rain rates vary in phase, with higher correlation between neighboring WDs. High temporal correlations （〉0.8） exist between WD and the combined algorithms （3B42, 3B43 and TCA for both V5 and V6） while satellite instrument algorithms （PR, TMI and TCI） are correlated best among themselves at the monthly scale. Paired t-tests of the monthly time series show that V5 3B42 and TMI are statistically different from the WD rain rates while no significant difference exists between WD and the other products. The agreements between the TRMM satellite and WD gauge estimates are best for the spring and fall and worst for winter and summer. The reduction in V6 TMI （-7.4%） and TPR （-31%） rain rates （compared to V5） results in better agreement between WD estimates and TMI in winter and TPR during summer.

Utilize cloud computing to support dust storm forecasting

The simulations and potential forecasting of dust storms are of significant interest to public health and environment sciences.Dust storms have interannual variabilities and are typical disruptive events.The computing platform for a dust storm forecasting operational system should support a disruptive fashion by scaling up to enable high-resolution forecasting and massive public access when dust storms come and scaling down when no dust storm events occur to save energy and costs.With the capability of providing a large,elastic,and virtualized pool of computational resources,cloud computing becomes a new and advantageous computing paradigm to resolve scientific problems traditionally requiring a large-scale and high-performance cluster.This paper examines the viability for cloud computing to support dust storm forecasting.Through a holistic study by systematically comparing cloud computing using Amazon EC2 to traditional high performance computing(HPC)cluster,we find that cloud computing is emerging as a credible solution for(1)supporting dust storm forecasting in spinning off a large group of computing resources in a few minutes to satisfy the disruptive computing requirements of dust storm forecasting,(2)performing high-resolution dust storm forecasting when required,(3)supporting concurrent computing requirements,(4)supporting real dust storm event forecasting for a large geographic domain by using recent dust storm event in Phoniex,05 July 2011 as example,and(5)reducing cost by maintaining low computing support when there is no dust storm events while invoking a large amount of computing resource to perform high-resolution forecasting and responding to large amount of concurrent public accesses.