Precipitation Intercomparison Project Research Articles

Development of the Pan-Arctic Snowfall Reconstruction: New Land-Based Solid Precipitation Estimates for 1940–99

Abstract A new product, the Pan-Arctic Snowfall Reconstruction (PASR), is developed to address the problem of cold season precipitation gauge biases for the 1940–99 period. The method used to create the PASR is different from methods used in other large-scale precipitation data products and has not previously been employed for estimating pan-arctic snowfall. The NASA Interannual-to-Seasonal Prediction Project Catchment Land Surface Model is used to reconstruct solid precipitation from observed snow depth and surface air temperatures. The method is tested at four stations in the United States and Canada where results are examined in depth. Reconstructed snowfall at Dease Lake, British Columbia, and Barrow, Alaska, is higher than gauge observations. Reconstructed snowfall at Regina, Saskatchewan, and Minot, North Dakota, is lower than gauge observations, probably because snow is transported by wind out of the Prairie region and enters the hydrometeorological cycle elsewhere. These results are similar to gauge biases estimated by a water budget approach. Reconstructed snowfall is consistently higher than snowfall from the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) but does not have a consistent relationship with snowfall derived from the WMO Solid Precipitation Intercomparison Project correction algorithms. Advantages of the PASR approach include that 1) the assimilation of snow depth observations captures blowing snow where it is deposited and 2) the modeling approach takes into account physical snowpack evolution. These advantages suggest that the PASR product could be a valuable alternative to statistical gauge corrections and that arctic ground-based solid precipitation observing networks might emphasize snow depth measurements over gauges.

Remote Sensing of Precipitation on the Tibetan Plateau Using the TRMM Microwave Imager

The Tibetan Plateau is a unique location for studying the global climate and China’s severe weather. The precipitation on the Tibetan Plateau can be studied conveniently with the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI). It is shown that the TMI brightness temperature at 85 GHz in the vertical polarization (TB 85V) is negatively correlated to the surface rain rate, but a very low value of TB 85V does not correspond to very intense surface rain rates on the Tibetan Plateau, a result that is different from what is observed in other areas of the world. For surface precipitation retrieval on the Tibetan Plateau from TMI, the effect from snow cover on precipitation retrieval is removed before analysis of precipitation. Using the dynamic cluster K-mean method, five categories of surface types and rain areas are identified on the Tibetan Plateau: dry soil, wet soil, water area, stratiform rain area, and convective rain area. The precipitation areas are screened by classification before the precipitation retrieval. Two datasets of rain-free areas and precipitation areas are formed after surface classification. Based on the dataset of rain-free areas, the value of TB85V can be simulated well by TB10V ,T B 19V, and TB21V when it is not raining. By means of the dataset of precipitation areas, it is revealed that the scattering index over land (SI L) is positively correlated and the polarization-corrected brightness temperature at 85 GHz (PCT85) is negatively correlated with the surface rain rate. With SI L, PCT85, and their combinations as retrieval algorithms, three precipitation retrieval formulas are proposed in which the SI L algorithm is most suitable for small rain retrieval, the PCT 85 algorithm is most suitable for moderate rain retrieval, and the combined SIL and PCT85 algorithm is most suitable for relatively large rain retrieval on the Tibetan Plateau. By means of two thresholds, 265 and 245 K, for TB85V, the combination of the three formulas is applied to precipitation retrieval on the Tibetan Plateau during the Tibetan Plateau Experiment Intensive Observing Period of 1998, resulting in acceptable and encouraging surface rain-rate retrievals. Intercomparison among the TMI algorithms and the 17 Special Sensor Microwave Imager algorithms from the second Precipitation Intercomparison Project demonstrates that the comprehensive application of the TMI algorithms has good precision and error index and is suitable for precipitation retrieval on the Tibetan Plateau.

Intercomparison of Global Precipitation Products: The Third Precipitation Intercomparison Project (PIP–3)

A set of global, monthly rainfall products has been intercompared to understand the quality and utility of the estimates. The products include 25 observational (satellite-based), four model and two climatological products. The results of the intercomparison indicate a very large range (factor of two or three) of values when all products are considered. The range of values is reduced considerably when the set of observational products is limited to those considered quasi-standard. The model products do significantly poorer in the tropics, but are competitive with satellite-based fields in mid-latitudes over land. Over ocean, products are compared to frequency of precipitation from ship observations. The evaluation of the observational products point to merged data products (including rain gauge information) as providing the overall best results.

Precipitation measurement using DMSP SSM/I data and the third precipitation intercomparison project (PIP-3)

The Defense Meteorological Satetlite Program (DMSP) is described and information is provided about the spacecraft, its environment, ground data system, global visible and infrared cloud data, The Third precipitation Intercomparison Project (PIP-3) objectives, principles of operation, serisof specifications and calibration ittformation of the SSM/I are discussed. Finally, some results of the Third Precipitation Intercornparison Project are presented.

Uncertainty analysis of satellite rainfall algorithms over the tropical Pacific

This paper describes the uncertainty analysis applied to comparisons between satellite monthly rainfall estimates and twenty 2.5° × 2.5° box rainfall estimates constructed from Pacific atoll‐sited rain gauge sites. This work was done in conjunction with NASA's WETNET Precipitation Intercomparison Project 3 (PIP 3), and was conducted by personnel at the Environmental Verification and Analysis Center (EVAC) at the University of Oklahoma. Motivation for this work stemmed from the need to supply surface areal rainfall estimates of known accuracy for comparison with satellite rainfall estimates and the need to use these estimates to quantify the uncertainty of the comparisons. This allowed for the discrimination of the value of various statistics associated with regression analysis, using different satellite algorithms, and for the quantification of the regression statistics computed by accounting for rain gauge uncertainty. To our knowledge this type of work has not been performed for previous satellite rainfall intercomparison programs.

The Second Precipitation Intercomparison Project (PIP-2)

The Second Precipitation Intercomparison Project (PIP-2)

Results of WetNet PIP-2 Project

The second WetNet Precipitation Intercomparison Project (PIP-2) evaluates the performance of 20 satellite precipitation retrieval algorithms, implemented for application with Special Sensor Microwave/Imager (SSM/I) passive microwave (PMW) measurements and run for a set of rainfall case studies at full resolution‐instantaneous space‐timescales. The cases are drawn from over the globe during all seasons, for a period of 7 yr, over a 608N‐ 178S latitude range. Ground-based data were used for the intercomparisons, principally based on radar measurements but also including rain gauge measurements. The goals of PIP-2 are 1) to improve performance and accuracy of different SSM/I algorithms at full resolution‐instantaneous scales by seeking a better understanding of the relationship between microphysical signatures in the PMW measurements and physical laws employed in the algorithms; 2) to evaluate the pros and cons of individual algorithms and their subsystems in order to seek optimal ‘‘front-end’’ combined algorithms; and 3) to demonstrate that PMW algorithms generate acceptable instantaneous rain estimates. It is found that the bias uncertainty of many current PMW algorithms is on the order of 630%. This level is below that of the radar and rain gauge data specially collected for the study, so that it is not possible to objectively select a best algorithm based on the ground data validation approach. By decomposing the intercomparisons into effects due to rain detection (screening) and effects due to brightness temperature‐rain rate conversion, differences among the algorithms are partitioned by rain area and rain intensity. For ocean, the screening differences mainly affect the light rain rates, which do not contribute significantly to area-averaged rain rates. The major sources of differences in mean rain rates between individual algorithms stem from differences in how intense rain rates are calculated and the maximum rain rate allowed by a given algorithm. The general method of solution is not necessarily the determining factor in creating systematic rain-rate differences among groups of algorithms, as we find that the severity of the screen is the dominant factor in producing systematic group differences among land algorithms, while the input channel selection is the dominant factor in producing systematic group differences among ocean algorithms. The significance of these issues are examined through what is called ‘‘fan map’’ analysis. The paper concludes with a discussion on the role of intercomparison projects in seeking improvements to algorithms, and a suggestion on why moving beyond the ‘‘ground truth’’ validation approach by use of a calibration-quality forward model would be a step forward in seeking objective evaluation of individual algorithm performance and optimal algorithm design.

An Investigation of the Relationship between Emission and Scattering Signals in SSM/I Data

To provide guidance for the development of satellite microwave rainfall-retrieval algorithms, the basic relationships between emission and scattering signals in natural clouds must be understood. In this study, the relationship between two parameters observed from microwave satellite data—the polarization difference at 19 GHz D and the polarization-corrected temperature PCT—is investigated over the global ocean on a monthly and 5° (lat) × 5° (long) mean basis. Using data from January and July 1993, the occurrence frequencies and latitudinal variation and horizontal distribution of the D–PCT relationships are investigated. The D–PCT slope is studied by dividing the entire weather range into three regimes: nonprecipitation, light precipitation, and heavy precipitation. The analysis shows that small variation of PCT in the nonprecipitation regime could be achieved by employing a variable coefficient in the PCT definition equation. The slopes in the light precipitation regime are latitude dependent. Although the interpretation is inconclusive, it is felt that the differences in the fractional coverage and the rain layer depth in different latitudes is responsible for the latitudinal dependence. No clear latitudinal dependence of slopes in the heavy precipitation regime is found. The connection of the D–PCT relationship to the performances of an emission-based and a scattering-based rainfall algorithm are investigated using the Second WetNet Precipitation Intercomparison Project rainfall cases. The results of this study emphasize the necessity of incorporating the scattering signal in rainfall rate retrieval algorithms. Additionally, the D–PCT slope information can be used to help categorize precipitation types, which may be useful in determining the specific algorithm best used for a certain precipitation type and/or regime.

A Statistical Modeling Approach to Passive Microwave Rainfall Retrieval

Abstract A new empirical algorithm for retrieving rainfall rates from passive microwave (particularly Special Sensor Microwave/Imager) data is presented. Errors caused by spatial and temporal variation of surface temperature, emissivity, and atmospheric effects are minimized by modeling the nonraining brightness temperatures within 0.5° latitude by 0.5° longitude regions based on the statistics of the satellite data from the whole of the month prior to the date of interest. Displacement of data away from the modeled relationship by more than a threshold value, calculated from the standard deviation of the nonraining data, is detected as rainfall. The algorithm is able to detect rainfall over land, sea, and coasts. An initial calibration and validation is performed using data from WetNet Precipitation Intercomparison Project 2 over the British Isles. To overcome collocation errors during calibration and validation, techniques are presented for matching radar rain pixels with appropriate satellite data. The...

The development of composite algorithms for global rainfall estimation using data from the DMSP SSM/I

Considerable variation in the performance of passive microwave global rainfall algorithms, both spatially and temporally,was revealed by the first WetNet Precipitation Intercomparison Project, PIP-1, with no one algorithm achieving the best results, in all locations, and all the time. In this paper a Composite Algorithm Procedure is described for the Special Sensor Microwave/Imager (SSM/I) algorithms submitted to PIP-1, that attempts through combining the best algorithm results from different regions of the globe to achieve better overall global rainfall estimates than are possible from any individual algorithm alone. The Composite Algorithm Procedure (CAP) involves the segmentation of the globe into homogeneous regions, the production of validation statistics for the various algorithm results in the different regions, and the identification of combinations of algorithms which perform best globally. The segmentations were based on aspects of the spatial and temporal variability of rainfall, or the microwave properties of the surfaces of the Earth. Initial results for the Composite Algorithm Procedure are presented for a sample month (October 1987): these confirm that improved global rainfall products can be produced in this way. Code detailing a selected Composite Algorithm based on the segmentation method of the microwave properties of the Earth has been supplied to the WetNet Support Group at the Marshall Space Flight Center, Huntsville, Alabama, for experimental, regular production of global rainfall data sets on a near real-time basis.

An intercomparison of oceanic precipitation frequencies from 10 special sensor microwave/imager rain rate algorithms and shipboard present weather reports

Oceanic precipitation frequencies produced by 10 satellite microwave algorithms submitted to the first Precipitation Intercomparison Project are compared with frequencies derived from ship reports. It is found that most algorithms yield distributions in reasonable agreement with the ship‐derived climatology at low latitudes, but the majority fail to register more than very infrequent (∼1%) precipitation at latitudes poleward of about 45°, in contrast to the much larger (∼10%) frequencies indicated by surface reports. Only two algorithms, both of which were based on the brightness temperature transformations of Petty [1994a, b], appear to approximately reproduce ship‐observed precipitation frequency patterns at both tropical and high latitudes. All 10 algorithms, however, appear to have difficulty producing realistic precipitation frequencies in the “dry” subtropical trade belts. This problem is tentatively attributed to the unique physical characteristics of precipitation in those regions.

Estimation of global precipitation using the ECMWF analysis‐forecasting scheme

Abstract The ECMWF analysis‐forecasting scheme used to provide inputs to WetNet's first Precipitation Intercomparison Project (PIP‐1) is briefly described. Aspects connected with the model's ability to simulate precipitation are discussed in more detail. Inconsistencies between the use of observed data and the model formulation lead to a spin‐up in the short‐range forecasts which has severe impacts on the precipitation. The shortest forecast ranges, e.g. 12–36 hour means, may best reflect the variability of precipitation in time and space, whereas more extended ranges, e.g. day 2–3 forecast means, may be better when the actual amounts on larger scales are of concern.

The first WetNet Precipitation intercomparison project: Generation of results

The first WetNet Precipitation intercomparison project: Generation of results

The first WetNet precipitation intercomparison project (PIP‐1): Interpretation of results

The first WetNet Precipitation Intercomparison Project (PIP‐1) was designed as part of the activity of WetNet's Precipitation Working Group, and intended to advance the science of global rainfall monitoring primarily through evaluations of existing passive microwave algorithms, both in relation to each other and also against conventional (rain gauge) data sets. In PIP‐1, intercomparisons of global rainfall estimates for August, September, October and November 1987 have been undertaken for 15 algorithms based on passive microwave DMSP‐SSM/I image data, one based on passive microwave NOAA‐MSU vertical profile data, one infrared image‐based algorithm, one combined passive‐based microwave/infrared imager algorithm, and one numerical weather prediction model, plus rainfall observations from continental rain gauges and from Pacific Atoll rain gauges. This paper begins by summarising the objectives of PIP‐1, the problems facing it, and its resulting intercomparison strategy. It then describes, exemplifies, and discusses results obtained by both qualitative and quantitative means for global, continental, and oceanic regions, before setting out some conclusions and recommendations for further studies. No single method of global rainfall estimation was found to be always better than all others, but several methods were each generally better over some major regions of the world. In view of the relatively short history of development of rainfall algorithms based on passive microwave imagery, and the relatively infrequent data presently available to support such types of approach, some passive microwave image‐based algorithms are already remarkably successful for estimating monthly global rainfall, and will benefit from on‐going developments in both related science and technology. Further global intercomparison projects should be designed to build on the many lessons learned by participants in PIP‐1. At the same time, more effort must be directed towards the acquisition of independent, in situ data sets for better calibration and validation of the satellite‐based results.

Surface data sets used in WetNet's PIP‐1 from the Comprehensive Pacific Rainfall Data Base and the Global Precipitation Climatology Centre

Abstract For intercomparison purposes and validation of different satellite‐based precipitation estimates in the framework of the first WetNet Precipitation Intercomparison Project (PIP‐1), two surface precipitation data sets have been used. The Comprehensive Pacific Rainfall Data Base (CPRDB) has been assimilated specifically for the calibration and verification of satellite rainfall algorithms over parts of the tropical Pacific ocean. The CPRDB consists of daily rain gauge data from more than 250 sites in the tropical Pacific, most of which are atolls and low‐lying islands. The data set evaluated by the Global Precipitation Climatology Centre (GPCC), a central element of the World Climate Research Programme (WCRP) Global Precipitation Climatology Project (GPCP), provides areal mean monthly precipitation totals on a 2.5° grid on a global scale for climate research and the verification of climate models. Although the merged GPCC data set is global in extent, it is based on an objective analysis of rain ga...