Precipitation Gauge Observations Research Articles

Evaluation of Radar Precipitation Estimates from the National Mosaic and Multisensor Quantitative Precipitation Estimation System and the WSR-88D Precipitation Processing System over the Conterminous United States

Abstract This study evaluated 24-, 6-, and 1-h radar precipitation estimated from the National Mosaic and Multisensor Quantitative Precipitation Estimation System (NMQ) and the Weather Surveillance Radar-1988 Doppler (WSR-88D) Precipitation Processing System (PPS) over the conterminous United States (CONUS) for the warm season April–September 2009 and the cool season October 2009–March 2010. Precipitation gauge observations from the Automated Surface Observing System (ASOS) were used as the ground truth. Gridded StageIV multisensor precipitation estimates were applied for supplementary verification. The comparison of the two systems consisted of a series of analyses including the linear correlation coefficient (CC) and the root-mean-square error (RMSE) between the radar precipitation estimates and the gauge observations, large precipitation amount detection categorical scores, and the reliability of precipitation amount distribution. Data stratified for the 12 CONUS River Forecast Centers (RFCs) and for the cold rains events with bright-band effects were analyzed additionally. Major results are 1) the linear CC of NMQ versus ASOS are generally higher than that of PPS versus ASOS over CONUS, while the spatial variations stratified by the RFCs may switch with seasons; 2) compared to the precipitation distribution of ASOS, NMQ shows less deviation than PPS; 3) for the cold rains verified against ASOS, NMQ has higher CC and PPS has lower RMSE for 6-h and higher RMSE for 1-h cold rains; and 4) for the precipitation detection categorical scores, either NMQ or PPS can be superior, depending on the time interval and season. The verification against StageIV gridded precipitation estimates showed that NMQ consistently had higher correlations and lower biases than did PPS.

Direct 4D-Var Assimilation of NCEP Stage IV Radar and Gauge Precipitation Data at ECMWF

Abstract Direct four-dimensional variational data assimilation (4D-Var) of NCEP stage IV radar and gauge precipitation observations over the eastern United States has been developed and tested in ECMWF’s global Integrated Forecasting System. This is the natural extension of earlier work using a two-step 1D+4D-Var approach. Major aspects of the implementation are described and discussed in this paper. In particular, it is found that assimilating 6-h precipitation accumulations instead of the original hourly data substantially improves the behavior of 4D-Var, especially as regards the validity of the tangent-linear assumption. The comparison of background and analysis precipitation departures demonstrates that most of the information contained in the new precipitation observations is properly assimilated. Experiments run over the periods April–May and September–October 2009 also show that local precipitation forecasts become significantly better for ranges up to 12 h, which indicates that a genuine precipitation analysis can now be obtained over the eastern United States. Geopotential, temperature, moisture, and wind forecast scores are generally neutral or slightly positive for all regions of the globe and at all ranges, which is consistent with previous 1D+4D-Var results. The most crucial issue that remains unsolved is the treatment of nonprecipitating model background occurrences because of the corresponding absence of sensitivity in the linearized moist physics. For the moment, only points where both model background and observations are rainy are assimilated. Operational implementation using U.S. data is planned in 2011 and one can hope that new networks of radars (and maybe rain gauges) can be added in the 4D-Var assimilation process in the future.

Identification of runoff generation spatial distribution using conventional hydrologic gauge time series

As traditional hydrologic measurements, stream stage and precipitation gauge observations often have the benefit of long records. Even though they have good value for calibrating conceptual models of basin hydrologic units, they are not adequate for understanding basic (spatial) physical processes such as rainfall‐runoff transformation. Stream discharge estimated from stage measurements represents the aggregate outflow from the catchment draining at the measurement location. Processes governing runoff nevertheless vary considerably across the terrain. Hillslope recharge‐discharge patterns, surface water–groundwater interaction, and the emergence of riparian zones are examples of processes that vary across the basin. Their dynamics are not evident in stream gauge hydrographs. In this paper a methodology is introduced that allows the extraction of some spatial information from the analysis of precipitation and discharge time series. The marginal distribution of available soil water storage across the basin is evident in the subtle and nonlinear covariations between storm precipitation and storm runoff volumes in the time series. The approach is nonparametric in that it does not assume nor require a conceptual hydrologic model. The methodology is introduced and tested using both synthetic and observed gauge records. A scalar and nondimensional measure of hydrologic complexity is introduced to characterize the spatial variability of basin rainfall‐runoff response behavior.

'Down-to-Earth' modelling of equivalent surface precipitation using multisource data and radar

The estimation of surface rainfall from reflectivity data derived from weather radar has been much studied over many years. It is now clear that central to this problem is the adjustment of these data for the impacts of vertical variations in the reflectivity. In this paper a new procedure (known as Down-to-Earth, DTE) is proposed and tested for combining radar measurements aloft with information from a numerical weather-prediction (NWP) model and an analysis system. The procedure involves the exploitation of moist cloud physics in an attempt to account for physical processes impacting on precipitation during its descent from the height of radar echo measurements to the surface. The application of DTE leads to increased underestimation in the radar measurements compared to precipitation gauge observations at short and intermediate radar ranges (0-120 km), but is successful at reducing the bias at further ranges. However the application of DTE does not lead to significant decreases in the random error of the surface rain rate estimate. No improvement is made when attempting to account for the precipitation phase measured by radar. It is concluded that further work on radar data quality control, along with improvements to the NWP model, are essential to improve upon results using such a physically based procedure.

Large-scale atmospheric patterns associated with mesoscale features leading to extreme precipitation events in Northwestern Italy

The synoptic and large-scale atmospheric conditions for heavy rainfall events in Northwestern Italy are diagnosed through the joint analysis of surface precipitation gauges and reanalysis atmospheric fields. Quantiles of local surface gauge precipitation observations are used to estimate the much larger-scale composite maps (conditional mean fields) of vertically integrated moisture flux, low-level winds, sea-level pressure, and 500 hPa height across the Atlantic and European domains. Remarkably, coarse-resolution reanalysis data show distinct synoptic conditions for heavy precipitation in localized regions that are below the resolution of the reanalysis. In this paper the key attributes of the new approach that is based on the joint analysis of gridded reanalysis and station data are presented. The application of the methodology are used to establish supporting evidence for hydrometeorological processes that lead to extreme precipitation across Northwest Italy. The results confirm the role of large-scale flow features that interact with regional topography in producing localized extreme precipitation. Whereas previous studies were based on a few case studies (modeled or observational), in this study the approach to producing a large ensemble of cases and composite statistics are introduced.

Impact on Short-Range Precipitation Forecasts from Assimilation of Ground-Based GPS Zenith Total Delay and Rain Gauge Precipitation Observations

This paper assesses the impact on short-range quantitative precipitation forecasts (QPFs) of assimilating zenith total delay (ZTD) and rainfall observations associated with a winter storm that occurred from 5-6 December 1997 in southern California. Assimilation of hourly rainfall improves the threat score by more than 300% within the assimilation window, but such an improvement drops quickly to 30% or lower beyond this window. The assimilation of ZTD observations does not produce a rainfall distribution as close to the observations as does the assimilation of rainfall within the assimilation window (only a 34% improvement). However, improvement in the QPFs beyond the window from the ZTD experiment is comparable to that from the rainfall experiment. Assimilation of ZTD and rainfall observations modifies the thermodynamic structures of the atmosphere, favoring development of precipitation in the observed rainy areas. The horizontal and vertical wind velocities are also adjusted consistent with the precipitation process. A spectral analysis of observed and simulated hourly rainfall, as well as the model forecast difference with and without data assimilation, indicates that rainfall assimilation adjusts the model variables on smaller scales (25 to 50 km) while the ZTD assimilation adjusts the model variables mainly on larger scales (> 50 km).

Simulation of anomalous regional climate events with a variable‐resolution stretched‐grid GCM

The stretched‐grid approach provides an efficient downscaling and consistent interactions between global and regional scales due to using one variable‐resolution model for integrations. It is a workable alternative to the widely used nested‐grid approach introduced over a decade ago as a pioneering step in regional climate modeling. A variable‐resolution finite‐difference GCM (general circulation model) employing a stretched grid, with enhanced resolution over the United States as the area of interest, is used for simulating two anomalous regional climate events, the U.S. summer drought of 1988 and the U.S. summer flood of 1993. The special mode of integration using a stretched‐grid GCM and data assimilation system is developed which allows for imitating the nested‐grid framework within a stretched‐grid approach. The mode is useful for intercomparison purposes and for underlining the differences between these two approaches. The 1988 and 1993 integrations are performed for the 2 month period starting from mid‐May. Regional resolutions used in most of the experiments is 60 km. The major goal and the result of the study is obtaining the efficient downscaling over the area of interest. The monthly mean prognostic regional fields for the stretched‐grid integrations are remarkably close to those of the verifying analyses. Simulated precipitation patterns are successfully verified against gauge precipitation observations. The impact of finer 40 km regional resolution is investigated for the 1993 integration and an example of recovering subregional precipitation is presented. The obtained results show that the global variable‐resolution stretched‐grid approach is a viable candidate for regional and subregional climate studies and applications.