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M. R. P. Sapiano and P. A. Arkin

Abstract

The last several years have seen the development of a number of new satellite-derived, globally complete, high-resolution precipitation products with a spatial resolution of at least 0.25° and a temporal resolution of at least 3-hourly. These products generally merge geostationary infrared data and polar-orbiting passive microwave data to take advantage of the frequent sampling of the infrared and the superior quality of the microwave. The Program to Evaluate High Resolution Precipitation Products (PEHRPP) was established to evaluate and intercompare these datasets at a variety of spatial and temporal resolutions with the intent of guiding dataset developers and informing the user community regarding the error characteristics of the products. As part of this project, the authors have performed a subdaily intercomparison of five high-resolution datasets [Climate Prediction Center morphing (CMORPH) technique; Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis (TMPA); Naval Research Laboratory (NRL) blended technique; National Environmental Satellite, Data, and Information Service Hydro-Estimator; and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN)] with existing subdaily gauge data over the United States and the Pacific Ocean. Results show that these data are effective at representing high-resolution precipitation, with correlations against 3-hourly gauge data as high as 0.7 for CMORPH, which had the highest correlations with the validation data. Biases are relatively high for most of the datasets over land (apart from the TMPA, which is gauge adjusted) and ocean, with a general tendency to overestimate warm season rainfall over the United States and to underestimate rainfall over the tropical Pacific Ocean. Additionally, all the products studied faithfully resolve the diurnal cycle of precipitation when compared with the validation data.

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M. R. P. Sapiano, D. B. Stephenson, H. J. Grubb, and P. A. Arkin

Abstract

A physically motivated statistical model is used to diagnose variability and trends in wintertime (October–March) Global Precipitation Climatology Project (GPCP) pentad (5-day mean) precipitation. Quasigeostrophic theory suggests that extratropical precipitation amounts should depend multiplicatively on the pressure gradient, saturation specific humidity, and the meridional temperature gradient. This physical insight has been used to guide the development of a suitable statistical model for precipitation using a mixture of generalized linear models: a logistic model for the binary occurrence of precipitation and a Gamma distribution model for the wet day precipitation amount.

The statistical model allows for the investigation of the role of each factor in determining variations and long-term trends. Saturation specific humidity qs has a generally negative effect on global precipitation occurrence and with the tropical wet pentad precipitation amount, but has a positive relationship with the pentad precipitation amount at mid- and high latitudes. The North Atlantic Oscillation, a proxy for the meridional temperature gradient, is also found to have a statistically significant positive effect on precipitation over much of the Atlantic region. Residual time trends in wet pentad precipitation are extremely sensitive to the choice of the wet pentad threshold because of increasing trends in low-amplitude precipitation pentads; too low a choice of threshold can lead to a spurious decreasing trend in wet pentad precipitation amounts. However, for not too small thresholds, it is found that the meridional temperature gradient is an important factor for explaining part of the long-term trend in Atlantic precipitation.

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Renu Joseph, Thomas M. Smith, Mathew R. P. Sapiano, and Ralph R. Ferraro

Abstract

The importance of analyzing climate at high spatiotemporal resolution has been emphasized in the recent Intergovernmental Panel on Climate Change (IPCC) report. Several high-resolution analyses of global precipitation have recently been created to meet this need by combining high-quality passive microwave estimates with frequently sampled geosynchronous infrared estimates. A new daily 0.25° analysis has been developed at the Cooperative Institute for Climate Studies (CICS), called the CICS High-Resolution Optimally Interpolated Microwave Precipitation from Satellites (CHOMPS), which is based only on passive microwave satellite estimates. The analysis was developed using all available sensors and the most up-to-date common retrieval scheme. An important advantage of CHOMPS is, therefore, its consistency. The microwave estimates from the different sensors at hourly time scales are combined using optimum interpolation (OI), using estimates of the noise and spatial correlation scales and standardized analysis weights. This technique reduces the random errors while still capturing the tails of the distribution of precipitation and provides estimates of output error based on the noise-to-signal ratio of the inputs. Hourly values are combined to produce a daily average and error estimate. Evaluation of CHOMPS against surface-based observational data, such as the stage IV radar and the Tropical Atmosphere Ocean (TAO)/Triangle Trans-Ocean Buoy Network (TRITON) gauges, indicates that CHOMPS performs well, especially when compared to other high-resolution products. This analysis, therefore, highlights OI as a feasible methodology for the creation of a high-resolution precipitation product. The analysis begins in 1998 and has thus far been continued through 2007, making it a useful dataset to examine intraseasonal variability. When the period of the data becomes long enough, it will prove to be a useful dataset to study longer modes of climate variability.

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M. R. P. Sapiano, J. E. Janowiak, P. A. Arkin, H. Lee, T. M. Smith, and P. Xie

Abstract

The longest record of precipitation estimated from satellites is the outgoing longwave radiation (OLR) precipitation index (OPI), which is based on polar-orbiting infrared observations from the Advanced Very High Resolution Radiometer (AVHRR) instrument that has flown onboard successive NOAA satellites. A significant barrier to the use of these data in studies of the climate of tropical precipitation (among other things) is the large bias caused by orbital drift that is present in the OLR data. Because the AVHRR instruments are deployed on the polar-orbiting spacecraft, OLR observations are recorded at specific times for each earth location for each day. Discontinuities are caused by the use of multiple satellites with different observing times as well as the orbital drift that occurs throughout the lifetime of each satellite. A regression-based correction is proposed based solely on the equator crossing time (ECT). The correction allows for separate means for each satellite as well as separate coefficients for each satellite ECT. The correction is calculated separately for each grid box but is applied only at locations where the correction is correlated with the OLR estimate. Thus, the correction is applied only where deemed necessary.

The OPI is used to estimate precipitation from the OLR estimates based on the new corrected version of the OLR, the uncorrected OLR, and two earlier published corrected versions. One of the earlier corrections is derived by removing variations from AVHRR based on EOFs that are identified as containing spurious variations related to the ECT bias, whereas the other is based on OLR estimates from the High Resolution Infrared Radiation Sounder (HIRS) that have been corrected using diurnal models for each grid box. The new corrected version is shown to be free of nearly all of the ECT bias and has the lowest root mean square difference when compared to gauges and passive microwave estimates of precipitation. The EOF-based correction fails to remove all of the variations related to the ECT bias, whereas the correction based on HIRS removes much of the bias but appears to introduce erroneous trends caused by the water vapor signal to which these data are sensitive. The new correction for AVHRR OLR works well in the tropics where the OPI has the most skill, but users should be careful when interpreting trends outside this region.

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Thomas M. Smith, Phillip A. Arkin, Mathew R. P. Sapiano, and Ching-Yee Chang

Abstract

A monthly reconstruction of precipitation beginning in 1900 is presented. The reconstruction resolves interannual and longer time scales and spatial scales larger than 5° over both land and oceans. Because of different land and ocean data availability, the reconstruction combines two separate historical reconstructions. One analyzes interannual variations directly by fitting gauge-based anomalies to large-scale spatial modes. This direct reconstruction is used for land anomalies and interannual oceanic anomalies. The other analyzes annual and longer variations indirectly from correlations with analyzed sea surface temperature and sea level pressure. This indirect reconstruction is used for oceanic variations with time scales longer than interannual. In addition, a method of estimating reconstruction errors is also presented.

Over land the reconstruction is a filtered representation of the gauge data with data gaps filled. Over oceans the reconstruction gives an estimate of the atmospheric response to changing temperature and pressure, combined with interannual variations. The reconstruction makes it possible to evaluate global precipitation variations for periods much longer than the satellite period, which begins in 1979. Evaluations show some large-scale similarities with coupled model precipitation variations over the twentieth century, including an increasing tendency over the century. The reconstructed land and sea trends tend to be out of phase at low latitudes, similar to the out-of-phase relationship for interannual variations. This reconstruction may be used for climate monitoring, for statistical climate studies of the twentieth century, and for helping to evaluate dynamic climate models. In the future the possibility of improving the reconstruction will be explored by further improving the analysis methods and including additional data.

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