Hydrometeorology Testbed Research Articles

The Impact of Precipitation Type Discrimination on Hydrologic Simulation: Rain–Snow Partitioning Derived from HMT-West Radar-Detected Brightband Height versus Surface Temperature Data

Abstract Hourly surface precipitation type (Ptype) grids (a total of 408 h from 1 December 2005 through April 20, 2006) were generated by mapping the elevation of the radar-detected brightband height (BBH) to terrain elevation during the 2005/06 observation period of the western Hydrometeorology Testbed (HMT-West) in the North Fork American River basin. BBH Ptype grids were compared to those derived by the standard National Weather Service (NWS) temperature threshold method. In this method, a fixed threshold temperature separating rain and snow was applied to hourly 4-km gridded temperature data. The BBH Ptype grids agreed well (>90%) with the temperature threshold–based grids below an elevation of 1524 m. The agreement dropped to below 60% above this elevation, and BBH Ptype produced more rainfall than the temperature-based Ptype. Continuous hourly streamflow simulations were generated using spatially lumped and distributed hydrologic models with and without the BBH Ptype data from 1 October 2005 through 30 September 2006. Simple insertion of BBH Ptype data did not always improve streamflow simulations for the 11 events examined relative to corresponding simulations using temperature threshold–derived precipitation type, possibly because of the use of the models calibrated with the temperature-based Ptype. The simple sensitivity test indicated simulations of both peak flows from midwinter storms and spring snowmelt runoff are affected by errors in precipitation type estimates.

Mesoscale Variations of the Atmospheric Snow Line over the Northern Sierra Nevada: Multiyear Statistics, Case Study, and Mechanisms

Abstract Observations from several mountain ranges reveal that the height of the transition from snowfall to rainfall, the snow line, can intersect the terrain at an elevation hundreds of meters below its elevation in the free air upwind. This mesoscale lowering of the snow line affects both the accumulation of mountain snowpack and the generation of storm runoff. A unique multiyear view of this behavior based on data from profiling radars in the northern Sierra Nevada deployed as part of NOAA’s Hydrometeorology Testbed is presented. Data from 3 yr of storms show that the mesoscale lowering of the snow line is a feature common to nearly all major storms, with an average snow line drop of 170 m. The mesoscale behavior of the snow line is investigated in detail for a major storm over the northern Sierra Nevada. Comparisons of observations from sondes and profiling radars with high-resolution simulations using the Weather Research and Forecasting model (WRF) show that WRF is capable of reproducing the observed lowering of the snow line in a realistic manner. Modeling results suggest that radar profiler networks may substantially underestimate the lowering by failing to resolve horizontal snow line variations in close proximity to the mountainside. Diagnosis of model output indicates that pseudoadiabatic processes related to orographic blocking, localized cooling due to melting of orographically enhanced snowfall, and spatial variations in hydrometeor melting distance all play important roles. Simulations are surprisingly insensitive to model horizontal resolution but have important sensitivities to microphysical parameterization.

NOAA's Rapid Response to the Howard A. Hanson Dam Flood Risk Management Crisis

The Howard A. Hanson Dam (HHD) has brought flood protection to Washington's Green River Valley for more than 40 years and opened the way for increased valley development near Seattle. However, following a record high level of water behind the dam in January 2009 and the discovery of elevated seepage through the dam's abutment, the U.S. Army Corps of Engineers declared the dam “unsafe.” NOAA's Office of Oceanic and Atmospheric Research (OAR) and National Weather Service (NWS) worked together to respond rapidly to this crisis for the 2009/10 winter season, drawing from innovations developed in NWS offices and in NOAA's Hydrometeorology Test-bed (HMT). New data telemetry was added to 14 existing surface rain gauges, allowing the gauge data to be ingested into the NWS rainfall database. The NWS Seattle Weather Forecast Office produced customized daily forecasts, including longer-lead-time hydrologic outlooks and new decision support services tailored for emergency managers and the public, new capabilities ena...

The NOAA Hydrometeorology Testbed Soil Moisture Observing Networks: Design, Instrumentation, and Preliminary Results

Abstract The NOAA Hydrometeorology Testbed (HMT) program has deployed soil moisture observing networks in the watersheds of the Russian River and the North Fork (NF) of the American River in northern California, and the San Pedro River in southeastern Arizona. These networks were designed to serve the combined needs of the hydrological, meteorological, agricultural, and climatological communities for observations of soil moisture on time scales that range from minutes to decades. The networks are a major component of the HMT program that has been developed to accelerate the development and infusion of new observing technologies, modeling methods, and recent scientific research into the National Weather Service (NWS) offices and to help focus research and development efforts on key hydrological and meteorological forecast problems. These forecast problems are not only of interest to the NWS, but they also play a crucial role in providing input to water managers who work at the national, state, and local government levels to provide water for human consumption, agriculture, and other needs. The HMT soil moisture networks have been specifically designed to capture the changes in soil moisture that are associated with heavy precipitation events and runoff from snowpack during the melt season. This paper describes the strategies used to site the networks and sensors as well as the selection, testing, and calibration of the soil moisture probes. In addition, two illustrative examples of the data gathered by the networks are shown. The first example shows changes in soil moisture observed before and during a flood event on the Babocomari River tributary of the San Pedro River near Sierra Vista, Arizona, on 23 July 2008. The second example examines a 5-yr continuous time series of soil moisture gathered at Healdsburg, California. The time series illustrates the transition from a multiyear wet period to exceptionally dry conditions from a soil moisture perspective.

Assessment of Extreme Quantitative Precipitation Forecasts and Development of Regional Extreme Event Thresholds Using Data from HMT-2006 and COOP Observers

Abstract Extreme precipitation events, and the quantitative precipitation forecasts (QPFs) associated with them, are examined. The study uses data from the Hydrometeorology Testbed (HMT), which conducted its first field study in California during the 2005/06 cool season. National Weather Service River Forecast Center (NWS RFC) gridded QPFs for 24-h periods at 24-h (day 1), 48-h (day 2), and 72-h (day 3) forecast lead times plus 24-h quantitative precipitation estimates (QPEs) from sites in California (CA) and Oregon–Washington (OR–WA) are used. During the 172-day period studied, some sites received more than 254 cm (100 in.) of precipitation. The winter season produced many extreme precipitation events, including 90 instances when a site received more than 7.6 cm (3.0 in.) of precipitation in 24 h (i.e., an “event”) and 17 events that exceeded 12.7 cm (24 h)−1 [5.0 in. (24 h)−1]. For the 90 extreme events {>7.6 cm (24 h)−1 [3.0 in. (24 h)−1]}, almost 90% of all the 270 QPFs (days 1–3) were biased low, increasingly so with greater lead time. Of the 17 observed events exceeding 12.7 cm (24 h)−1 [5.0 in. (24 h)−1], only 1 of those events was predicted to be that extreme. Almost all of the extreme events correlated with the presence of atmospheric river conditions. Total seasonal QPF biases for all events {i.e., ≥0.025 cm (24 h)−1 [0.01 in. (24 h)−1]} were sensitive to local geography and were generally biased low in the California–Nevada River Forecast Center (CNRFC) region and high in the Northwest River Forecast Center (NWRFC) domain. The low bias in CA QPFs improved with shorter forecast lead time and worsened for extreme events. Differences were also noted between the CNRFC and NWRFC in terms of QPF and the frequency of extreme events. A key finding from this study is that there were more precipitation events >7.6 cm (24 h)−1 [3.0 in. (24 h)−1] in CA than in OR–WA. Examination of 422 Cooperative Observer Program (COOP) sites in the NWRFC domain and 400 in the CNRFC domain found that the thresholds for the top 1% and top 0.1% of precipitation events were 7.6 cm (24 h)−1 [3.0 in. (24 h)−1] and 14.2 cm (24 h)−1 [5.6 in. (24 h)−1] or greater for the CNRFC and only 5.1 cm (24 h)−1 [2.0 in. (24 h)−1] and 9.4 cm (24 h)−1 [3.7 in. (24 h)−1] for the NWRFC, respectively. Similar analyses for all NWS RFCs showed that the threshold for the top 1% of events varies from ∼3.8 cm (24 h)−1 [1.5 in. (24 h)−1] in the Colorado Basin River Forecast Center (CBRFC) to ∼5.1 cm (24 h)−1 [3.0 in. (24 h)−1] in the northern tier of RFCs and ∼7.6 cm (24 h)−1 [3.0 in. (24 h)−1] in both the southern tier and the CNRFC. It is recommended that NWS QPF performance in the future be assessed for extreme events using these thresholds.

Evaluation of Incremental Improvements to Quantitative Precipitation Estimates in Complex Terrain

Abstract Advanced remote sensing and in situ observing systems employed during the Hydrometeorological Testbed experiment on the American River basin near Sacramento, California, provided a unique opportunity to evaluate correction procedures applied to gap-filling, experimental radar precipitation products in complex terrain. The evaluation highlighted improvements in hourly radar rainfall estimation due to optimizing the parameters in the reflectivity-to-rainfall (Z–R) relation, correcting for the range dependence in estimating R due to the vertical variability in Z in snow and melting-layer regions, and improving low-altitude radar coverage by merging rainfall estimates from two research radars operating at different frequencies and polarization states. This evaluation revealed that although the rainfall product from research radars provided the smallest bias relative to gauge estimates, in terms of the root-mean-square error (with the bias removed) and Pearson correlation coefficient it did not outperform the product from a nearby operational radar that used optimized Z–R relations and was corrected for range dependence. This result was attributed to better low-altitude radar coverage with the operational radar over the upper part of the basin. In these regions, the data from the X-band research radar were not available and the C-band research radar was forced to use higher-elevation angles as a result of nearby terrain and tree blockages, which yielded greater uncertainty in surface rainfall estimates. This study highlights the challenges in siting experimental radars in complex terrain. Last, the corrections developed for research radar products were adapted and applied to an operational radar, thus providing a simple transfer of research findings to operational rainfall products yielding significantly improved skill.

Short-Range Precipitation Forecasts from Time-Lagged Multimodel Ensembles during the HMT-West-2006 Campaign

Abstract High-resolution (3 km) time-lagged (initialized every 3 h) multimodel ensembles were produced in support of the Hydrometeorological Testbed (HMT)-West-2006 campaign in northern California, covering the American River basin (ARB). Multiple mesoscale models were used, including the Weather Research and Forecasting (WRF) model, Regional Atmospheric Modeling System (RAMS), and fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5). Short-range (6 h) quantitative precipitation forecasts (QPFs) and probabilistic QPFs (PQPFs) were compared to the 4-km NCEP stage IV precipitation analyses for archived intensive operation periods (IOPs). The two sets of ensemble runs (operational and rerun forecasts) were examined to evaluate the quality of high-resolution QPFs produced by time-lagged multimodel ensembles and to investigate the impacts of ensemble configurations on forecast skill. Uncertainties in precipitation forecasts were associated with different models, model physics, and initial and boundary conditions. The diabatic initialization by the Local Analysis and Prediction System (LAPS) helped precipitation forecasts, while the selection of microphysics was critical in ensemble design. Probability biases in the ensemble products were addressed by calibrating PQPFs. Using artificial neural network (ANN) and linear regression (LR) methods, the bias correction of PQPFs and a cross-validation procedure were applied to three operational IOPs and four rerun IOPs. Both the ANN and LR methods effectively improved PQPFs, especially for lower thresholds. The LR method outperformed the ANN method in bias correction, in particular for a smaller training data size. More training data (e.g., one-season forecasts) are desirable to test the robustness of both calibration methods.

The Impact of Different Physical Parameterizations and Their Interactions on Cold Season QPF in the American River Basin

Abstract The most significant precipitation events in California occur during the winter and are often related to synoptic-scale storms from the Pacific Ocean. Because of the terrain characteristics and the fact that the urban and infrastructural expansion is concentrated in lower elevation areas of the California Central Valley, a high risk of flooding is usually associated with these events. In the present study, the area of interest was the American River basin (ARB). The main focus of the present study was to investigate methods for Quantitative Precipitation Forecast (QPF) improvement by estimating the impact that various microphysical schemes, planetary boundary layer (PBL) schemes, and initialization methods have on cold season precipitation, primarily orographically induced. For this purpose, 3-km grid spacing Weather Research and Forecasting (WRF) model simulations of four Hydrometeorological Test bed (HMT) events were used. For each event, four different microphysical schemes and two different PBL schemes were used. All runs were initialized with both a diabatic Local Analysis and Prediction System (LAPS) “hot” start and 40-km eta analyses. To quantify the impact of physical schemes, their interactions, and initial conditions upon simulated rain volume, the factor separation methodology was used. The results showed that simulated rain volume was particularly affected by changes in microphysical schemes for both initializations. When the initialization was changed from the LAPS to the eta analysis, the change in the PBL scheme and corresponding synergistic terms (which corresponded to the interactions between different microphysical and PBL schemes) resulted in a statistically significant impact on rain volume. In addition, by combining model runs based on the knowledge about their impact on simulated rain volume obtained through the factor separation methodology, the bias in simulated rain volume was reduced.

The Utility of X-Band Polarimetric Radar for Quantitative Estimates of Rainfall Parameters

Abstract The utility of X-band polarimetric radar for quantitative retrievals of rainfall parameters is analyzed using observations collected along the U.S. west coast near the mouth of the Russian River during the Hydrometeorological Testbed project conducted by NOAA’s Environmental Technology and National Severe Storms Laboratories in December 2003 through March 2004. It is demonstrated that the rain attenuation effects in measurements of reflectivity (Ze) and differential attenuation effects in measurements of differential reflectivity (ZDR) can be efficiently corrected in near–real time using differential phase shift data. A scheme for correcting gaseous attenuation effects that are important at longer ranges is introduced. The use of polarimetric rainfall estimators that utilize specific differential phase and differential reflectivity data often provides results that are superior to estimators that use fixed reflectivity-based relations, even if these relations were derived from the ensemble of drop size distributions collected in a given geographical region. Comparisons of polarimetrically derived rainfall accumulations with data from the high-resolution rain gauges located along the coast indicated deviation between radar and gauge estimates of about 25%. The ZDR measurements corrected for differential attenuation were also used to retrieve median raindrop sizes, D0. Because of uncertainties in differential reflectivity measurements, these retrievals are typically performed only for D0 > 0.75 mm. The D0 estimates from an impact disdrometer located at 25 km from the radar were in good agreement with the radar retrievals. The experience of operating the transportable polarimetric X-band radar in the coastal area that does not have good coverage by the National Weather Service radar network showed the value of such radar in filling the gaps in the network coverage. The NOAA X-band radar was effective in covering an area up to 40–50 km in radius offshore adjacent to a region that is prone to flooding during wintertime landfalling Pacific storms.