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In an attempt to develop a better simulation of the climatology of monsoon precipitation in climate models, this paper investigates the impacts of different convective closures on systematic biases of an Indian monsoon precipitation climatology in a high-resolution regional climate model. For this purpose, the Weather Research Forecast (WRF) model is run at 45- and 15-km (two-way nested) resolution with three convective parameterization schemes, namely the Grell–Devenyi (GD), the Betts–Miller–Janjić (BMJ), and the Kain–Fritsch (KF), for the period 1 May–31 October 2001–07. The model is forced with the NCEP–NCAR reanalysis data as the initial and boundary conditions. The simulated June–September (JJAS) mean monsoon rainfall with the three convective schemes is compared with the observations. KF is found to have a high moist bias over the central and western coastal Indian region while GD shows the opposite. Among the three, BMJ is able to produce a reasonable mean monsoon pattern. In an attempt to get further insight into the seasonal bias and its evolution, the probability distribution function (PDF) of different rain-rate categories and their percentage contribution to the seasonal total are computed. BMJ and KF underestimate the observations for lighter rain rates and overestimate for rain-rate categories of more than 10 mm day−1. GD shows an overestimation for lighter rain and an underestimation of PDF for moderate categories. The seasonal patterns of evolution of PDF plots of three rain-rate categories are analyzed to determine whether the convective schemes show any systematic bias throughout the season or if they have problems during certain phases of the monsoon. This shows that the GD systematically overestimates the lighter rain rate and underestimates the moderate rain rate throughout the season, whereas BMJ and KF have problems in the initial stages. The heavy rain category is systematically overestimated by the KF compared to the other two. To further evaluate the proportionate contribution of each rain-rate bin to the total rain, the percentage contribution of each rain rate to the seasonal total is computed. Analyzing all the rain-rate simulations produced by the three schemes, it is found that KF has a moist bias and GD has a dry bias in the spatiotemporal distribution of the monsoon precipitation. Further, this paper investigates the causes behind the mean monsoon precipitation bias. It is shown that GD produces a model climate where the vertical velocity is less than that of the observations up to 500 hPa and the vertically integrated moist instability is also weaker. KF, on the other hand, shows a higher than the observed vertical velocity and a stronger moist instability. Along with this, the vertical profile of heating suggests a warmer middle level in the KF case and significantly reduced midlevel heating for GD. Thus, KF (GD) has produced a model atmosphere that has a stronger (weaker) convective instability to produce the observed bias in the model precipitation. BMJ is found to simulate a reasonable heating profile, along with the realistic moist instability and seasonal cycle of evaporation and condensation. Insight derived from the analysis is expected to help improve the convective parameterizations. [ABSTRACT FROM AUTHOR]

Quantitative precipitation forecasting (QPF) continues to be a significant challenge in operational forecasting, particularly in regions susceptible to extreme precipitation events. St. John’s, Newfoundland, Canada (CYYT), is affected frequently by such events, particularly in the cool season (October–April). The 50 median events in the extreme (>33.78 mm during a 48-h period) precipitation event category are selected for further analysis. A manual synoptic typing is performed on these 50 events, using two separate methodologies to partition events. The first method utilizes a Lagrangian backward air parcel trajectory analysis and the second method utilizes the evolution of dynamically relevant variables, including 1000–700-hPa horizontal temperature advection, 1000–700-hPa (vector) geostrophic frontogenesis, and 700–400-hPa absolute vorticity advection. Utilizing the first partitioning method, it is found that south cases are characterized by a strong anticyclone downstream of St. John’s, southwest events are synoptically similar to the overall extreme composite and are marked by a strong cyclone that develops in the Gulf of Mexico, while west events are characterized by a weak Alberta clipper system that intensifies rapidly upon reaching the Atlantic Ocean. The second partitioning method suggests that while cyclone events are dominated by the presence of a rapidly developing cyclone moving northeastward toward St. John’s, frontal events are characterized by the presence of a strong downstream anticyclone and deformation zone at St. John’s. It is the hope of the authors that the unique methodology and results of the synoptic typing in this paper will aid forecasters in identifying certain characteristics of future precipitation events at St. John’s and similar stations. [ABSTRACT FROM AUTHOR]

As one of the most widely used skill scores for model quantitative precipitation forecast (QPF) verification and evaluation, the equitable threat score ETS differs from the threat score TS in that the random hits R are removed from its calculation. In practice, however, when applied to a set of verification points confined to small areas, the random assumption often becomes increasingly questionable or even invalid in larger events. As a result, the random hits are overestimated and the ETS becomes biased and not indicative of model skills. In this paper, such an issue is explored and demonstrated through the example of Taiwan with steep topography from Typhoon Morakot (2009) and mei-yu heavy-rainfall cases. It is found that the ETS is affected more seriously and scaled down by at least about 0.1 compared to the TS whenever the rain area occupies roughly 20% or more of the total verification area (if the random assumption of R is invalid). As such conditions often occur for small areas, it is vital to estimate R as correctly as possible for the ETS to work properly. A simple solution is offered by using all gridpoint values from the entire model domain, rather than just a small subset falling into the verification area, to estimate the random hit rate in the forecast. While the ETS remains unaltered in its definition, the proposed method yields the best estimates of R available by using the largest sample size from the model and subsequently better-behaved ETS values and is, therefore, recommended for all applications of ETS for small verification areas. [ABSTRACT FROM AUTHOR]

It is hypothesized that high-resolution, accurate representations of surface properties such as soil moisture and sea surface temperature are necessary to improve simulations of summertime pulse-type convective precipitation in high-resolution models. This paper presents model verification results of a case study period from June to August 2008 over the southeastern United States using the Weather Research and Forecasting numerical weather prediction model. Experimental simulations initialized with high-resolution land surface fields from the National Aeronautics and Space Administration's (NASA) Land Information System (LIS) and sea surface temperatures (SSTs) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) are compared to a set of control simulations initialized with interpolated fields from the National Centers for Environmental Prediction's (NCEP) 12-km North American Mesoscale model. The LIS land surface and MODIS SSTs provide a more detailed surface initialization at a resolution comparable to the 4-km model grid spacing. Soil moisture from the LIS spinup run is shown to respond better to the extreme rainfall of Tropical Storm Fay in August 2008 over the Florida peninsula. The LIS has slightly lower errors and higher anomaly correlations in the top soil layer but exhibits a stronger dry bias in the root zone. The model sensitivity to the alternative surface initial conditions is examined for a sample case, showing that the LIS-MODIS data substantially impact surface and boundary layer properties. The Developmental Testbed Center's Meteorological Evaluation Tools package is employed to produce verification statistics, including traditional gridded precipitation verification and output statistics from the Method for Object-Based Diagnostic Evaluation (MODE) tool. The LIS-MODIS initialization is found to produce small improvements in the skill scores of 1-h accumulated precipitation during the forecast hours of the peak diurnal convective cycle. Because there is very little union in time and space between the forecast and observed precipitation systems, results from the MODE object verification are examined to relax the stringency of traditional gridpoint precipitation verification. The MODE results indicate that the LIS-MODIS-initialized model runs increase the 10 mm h−1 matched object areas ('hits') while simultaneously decreasing the unmatched object areas ('misses' plus 'false alarms') during most of the peak convective forecast hours, with statistically significant improvements of up to 5%%. Simulated 1-h precipitation objects in the LIS-MODIS runs more closely resemble the observed objects, particularly at higher accumulation thresholds. Despite the small improvements, however, the overall low verification scores indicate that much uncertainty still exists in simulating the processes responsible for airmass-type convective precipitation systems in convection-allowing models. [ABSTRACT FROM AUTHOR]

This paper evaluates the quality of real-time seasonal probabilistic forecasts of the extreme 15%% tails of the climatological distribution of temperature and precipitation issued by the International Research Institute for Climate and Society (IRI) from 1998 through 2009. IRI's forecasts have been based largely on a two-tiered multimodel dynamical prediction system. Forecasts of the 15%% extremes have been consistent with the corresponding probabilistic forecasts for the standard tercile-based categories; however, nonclimatological forecasts for the extremes have been issued sparingly. Results indicate positive skill in terms of resolution and discrimination for the extremes forecasts, particularly in the tropics. Additionally, with the exception of some overconfidence for extreme above-normal precipitation and a strong cool bias for temperature, reliability analyses suggest generally good calibration. Skills for temperature are generally higher than those for precipitation, due both to correct forecasts of increased probabilities of extremely high (above the upper 15th percentile) temperatures associated with warming trends, and to better discrimination of interannual variability. However, above-normal temperature extremes were substantially underforecast, as noted also for the IRI's tercile forecasts. [ABSTRACT FROM AUTHOR]

A data-driven approach for obtaining near-term (2–20 years) regional temperature and precipitation projections utilizing local historical observations was established in this study to facilitate civil and environmental engineering applications. Given the unique characteristics of temporal correlation and skewness exhibited in individual time series of temperature and precipitation variables, a statistical time series forecasting technique was developed based on the autoregressive integrated moving average (ARIMA) model. Annual projections obtained from the ARIMA model—depending on individual series—can be interpreted as an integration of the most recent observations and the long-term historical trend. In addition to annual temperature and precipitation forecasts, methods of estimating confidence intervals for different return periods and simulating future daily temperature and precipitation were developed to extend the applicability for use in engineering. Quantitative comparisons of annual temperature and precipitation forecasts developed from the ARIMA model and other common statistical techniques such as a linear trend method were performed. Results suggested that while the ARIMA model cannot outperform all other techniques for all evaluated climate indices, the ARIMA model in general provides more accurate projections—especially interval forecasts—and is more reliable than other common statistical techniques. With the use of the ARIMA-based statistical forecasting model, interpretable and reliable near-term, location-specific temperature and precipitation forecasts can be obtained for consideration of changing climate in civil and environmental engineering applications. [ABSTRACT FROM AUTHOR]

Two severe winter storms in 2016 and 2017 caused by landfalling atmospheric rivers over British Columbia (BC) are investigated in this study. Our main concern is the impact of hydrometeor drift on the orographic precipitation. It is shown that the dominant contribution to the windward orographic precipitation was from the horizontal moisture convergence. The precipitation distributions across southern BC were also influenced by the convergence/divergence of condensed water due to the wind-driven effect on hydrometeors. Observed hourly and daily precipitation amounts are used to verify the performances of three Canadian numerical weather prediction systems. Our results indicate that these operational systems were capable of predicting the general features of orographic precipitation in BC. However, the two coarse-resolution systems used a diagnostic precipitation scheme that does not fully simulate the hydrometeor drift process. The High-Resolution Deterministic Prediction System (HRDPS) with a prognostic precipitation scheme was substantially more accurate and skillful in predicting the upwind precipitation as well as the spillover of precipitation on the leeward slopes for these two storms. There was evidence suggesting that the spillover effect was overpredicted by the HRDPS due to a systematic bias originating in the model microphysics. This problem has been improved in the current HRDPS with a new microphysics scheme. Based on our atmospheric water balance analysis, we also proposed two postprocessing schemes that could be applied to improve the quantitative precipitation forecasts of the diagnostic precipitation schemes. [ABSTRACT FROM AUTHOR]

A competitive neural network known as the self-organizing map (SOM) is used to objectively identify synoptic patterns in the North American Regional Reanalysis (NARR) for warm-season (April–September) precipitation events over the Southern and Northern Great Plains (SGP/NGP) from 2007 to 2014. Classifications for both regions demonstrate contrast in dominant synoptic patterns ranging from extratropical cyclones to subtropical ridges, all of which have preferred months of occurrence. Precipitation from deterministic Weather Research and Forecasting (WRF) Model simulations run by the National Severe Storms Laboratory (NSSL) are evaluated against National Centers for Environmental Prediction (NCEP) Stage IV observations. The SGP features larger observed precipitation amount, intensity, and coverage, as well as better model performance than the NGP. Both regions' simulated convective rain intensity and coverage have good agreement with observations, whereas the stratiform rain (SR) is more problematic with weaker intensity and larger coverage. Further evaluation based on SOM regimes shows that WRF bias varies with the type of meteorological forcing, which can be traced to differences in the diurnal cycle and properties of stratiform and convective rain. The higher performance scores are generally associated with the extratropical cyclone condition than the subtropical ridge. Of the six SOM classes over both regions, the largest precipitation oversimulation is found for SR dominated classes, whereas a nocturnal negative precipitation bias exists for classes featuring upscale growth of convection. [ABSTRACT FROM AUTHOR]

The tropical west Pacific Ocean and the Philippines are often affected by tropical cyclones (TCs), with threats to human life and of severe economic damage. The performance of the Met Office global operational forecasts at predicting TC-related precipitation is examined between 2006 and 2017, the first time total TC rainfall has been analyzed in a long-term forecast dataset. All precipitation falling within 5° of a TC track point is assumed to be part of the TC rainbands. Forecasts are verified against TC tracks from the JRA-55 reanalysis and precipitation from TRMM 3B42. In composites from the forecasts, the total precipitation (TC and non-TC) is too high and the TC-related precipitation is too low, over both ocean and the Philippines. These biases exist all year-round and generally worsen with lead time, but have improved in recent years with upgrades to the forecasting system. Biases in TC-related precipitation in the Philippines are attributable mainly to TC lifetime being too short over land and ocean and (over land) possibly to individual TCs producing too little rain. There are considerable biases in predicted large-scale conditions related to TC intensification, particularly too little lower-troposphere relative humidity and too strong vertical wind shear. The shear appears to have little impact on the amount of TC precipitation, but dry biases in humidity are consistent with dry biases in TC rainfall. The forecast system accurately reproduces the impact of the MJO on TC precipitation, relative to the forecasts' own climatology, potentially providing the opportunity for predictability out to several weeks. [ABSTRACT FROM AUTHOR]

This article examines to what extent the NCEP Climate Forecast System (CFS) weeks 3–4 reforecasts reproduce the CFS Reanalysis (CFSR) storm-track properties, and if so, whether the storm-track behavior can contribute to the prediction of related winter weather in North America. The storm tracks are described by objectively tracking isentropic potential vorticity (PV) anomalies for two periods (base, 1983–2002; validation, 2003–10) to assess their value in a more realistic forecast mode. Statistically significant positive PV biases are found in the storm-track reforecasts. Removal of systematic errors is found to improve general storm-track features. CFSR and Reforecast (CFSRR) reproduces well the observed intensity and spatial distributions of storm-track-related near-surface winds, with small yet significant biases found in the storm-track regions. Removal of the mean wind bias further reduces the error on average by 12%. The spatial distributions of the reforecast precipitation correspond well with the reanalysis, although significant positive biases are found across the contiguous United States. Removal of the precipitation bias reduces the error on average by 25%. The bias-corrected fields better depict the observed variability and exhibit additional improvements in the representation of winter weather associated with strong-storm tracks (the storms with more intense PV). Additionally, the reforecasts reproduce the characteristic intensity and frequency of hazardous strong-storm winds. The findings suggest a potential use of storm-track statistics in the advancement of subseasonal-to-seasonal weather prediction in North America. [ABSTRACT FROM AUTHOR]

In the Weather Research and Forecasting (WRF) community, a standard model setup at a grid size smaller than 5 km excludes cumulus parameterization (CP), although it is unclear how to determine a cutoff grid size where convection permitting can be assumed adequate. Also, efforts to improve high-resolution precipitation forecasts in the range of 1–10 km (the so-called gray zone for parameterized precipitation physics) have recently been made. In this study, we attempt to statistically evaluate the skill of a gray-zone CP with a focus on the quantitative precipitation forecast (QPF) in the summertime. A WRF Model simulation with the gray-zone simplified Arakawa–Schubert (GSAS) CP at 3-km spatial resolution over East Asia is evaluated for the summer of 2013 and compared with the results from a conventional setup without CP. A statistical evaluation of the 3-month simulations shows that the GSAS demonstrates a typical distribution of the QPF skill, with high (low) scores and bias in the light (heavy) precipitation category. The WRF without CP seriously suppresses light precipitation events, but its skill for heavier categories is better. Meanwhile, a new set of precipitation data, which is simply averaged precipitation from the two simulations, demonstrates the best skill in all precipitation categories. Bearing in mind that high-resolution QPF requires essential challenges in model components, along with complexity in precipitating convection mechanisms over geographically different regions, this proposed method can serve as an alternative for improving the QPF for practical usage. [ABSTRACT FROM AUTHOR]

A continental United States (CONUS)-wide framework for analyzing quantitative precipitation forecasts (QPFs) from NWP models from the perspective of precipitation return period (RP) exceedances is introduced using threshold estimates derived from a combination of NOAA Atlas 14 and older sources. Forecasts between 2009 and 2015 from several different NWP models of varying configurations and spatial resolutions are analyzed to assess bias characteristics and forecast skill for predicting RP exceedances. Specifically, NOAA's Global Ensemble Forecast System Reforecast (GEFS/R) and the National Severe Storms Laboratory WRF (NSSL-WRF) model are evaluated for 24-h precipitation accumulations. The climatology of extreme precipitation events for 6-h accumulations is also explored in three convection-allowing models: 1) NSSL-WRF, 2) the North American Mesoscale 4-km nest (NAM-NEST), and 3) the experimental High Resolution Rapid Refresh (HRRR). The GEFS/R and NSSL-WRF are both found to exhibit similar 24-h accumulation RP exceedance climatologies over the U.S. West Coast to those found in observations and are found to be approximately equally skillful at predicting these exceedance events in this region. In contrast, over the eastern two-thirds of the CONUS, GEFS/R struggles to predict the predominantly convectively driven extreme QPFs, predicting far fewer events than are observed and exhibiting inferior forecast skill to the NSSL-WRF. The NSSL-WRF and HRRR are found to produce 6-h extreme precipitation climatologies that are approximately in accord with those found in the observations, while NAM-NEST produces many more RP exceedances than are observed across all of the CONUS. [ABSTRACT FROM AUTHOR]

The National Centers for Environmental Prediction (NCEP) stage IV quantitative precipitation estimates (QPEs) are used in many studies for intercomparisons including those for satellite QPEs. An overview of the National Weather Service precipitation processing system is provided here so as to set the stage IV product in context and to provide users with some knowledge as to how it is developed. Then, an assessment of the stage IV product over the period 2002-12 is provided. The assessment shows that the stage IV product can be useful for conditional comparisons of moderate-to-heavy rainfall for select seasons and locations. When evaluating the product at the daily scale, there are many discontinuities due to the operational processing at the radar site as well as discontinuities due to the merging of data from different River Forecast Centers (RFCs) that use much different processing algorithms for generating their precipitation estimates. An assessment of the daily precipitation estimates is provided based on the cumulative distribution function for all of the daily estimates for each RFC by season. In addition it is found that the hourly estimates at certain RFCs suffer from lack of manual quality control and caution should be used. [ABSTRACT FROM AUTHOR]

In winter weather, precipitation type is a pivotal characteristic because it determines the nature of most preparations that need to be made. Decisions about how to protect critical infrastructure, such as power lines and transportation systems, and optimize how best to get aid to people are all fundamentally precipitation-type dependent. However, current understanding of the microphysical processes that govern precipitation type and how they interplay with physics-based numerical forecast models is incomplete, degrading precipitation-type forecasts, but by how much? This work demonstrates the utility of crowd-sourced surface observations of precipitation type from the Meteorological Phenomena Identification Near the Ground (mPING) project in estimating the skill of numerical model precipitation-type forecasts and, as an extension, assessing the current model performance regarding precipitation type in areas that are otherwise without surface observations. In general, forecast precipitation type is biased high for snow and rain and biased low for freezing rain and ice pellets. For both the North American Mesoscale Forecast System and Global Forecast System models, Gilbert skill scores are between 0.4 and 0.5 and from 0.35 to 0.45 for the Rapid Refresh model, depending on lead time. Peirce skill scores for individual precipitation types are 0.7-0.8 for both rain and snow, 0.2-0.4 for freezing rain and freezing rain, and 0.25 or less for ice pellets. The Rapid Refresh model displays somewhat lower scores except for ice pellets, which are severely underforecast, compared to the other models. [ABSTRACT FROM AUTHOR]

Polarimetric radar observations provide information regarding the shape and size of scatterers in the atmosphere, which help users to differentiate between precipitation and nonprecipitation radar echoes. Identifying and removing nonprecipitation echoes in radar reflectivity fields is one critical step in radar-based quantitative precipitation estimation. An automated algorithm based on reflectivity, correlation coefficient, and temperature data is developed to perform reflectivity data quality control through a set of physically based rules. The algorithm was tested with a large number of real data cases across different geographical regions and seasons and showed a high accuracy (Heidke skill score of 0.83) in segregating precipitation and nonprecipitation echoes. The algorithm was compared with two other operational and experimental reflectivity quality control methodologies and showed a more effective removal of nonprecipitation echoes and a higher computational efficiency. The current methodology also demonstrated a satisfactory performance in a real-time national multiradar and multisensor system. [ABSTRACT FROM AUTHOR]

The hourly updating High-Resolution Rapid Refresh (HRRR) model is evaluated with regard to its ability to predict the areal extent of cold-season precipitation and accurately depict the timing and location of regions of snow, rain, and mixed-phase precipitation on the ground. Validation of the HRRR forecasts is performed using observations collected by the Automated Surface Observing System (ASOS) stations across the eastern two-thirds of the United States during the 2010-11 cold season. The results show that the HRRR is able to reliably forecast precipitation extent during the cold season. In particular, the location and areal extent of both snow and rain are very well predicted. Depiction of rain-to-snow transitions and freezing rain is reasonably good; however, the associated evaluation scores are significantly lower than for either snow or rain. The analyses suggest the skill in accurately depicting precipitation extent and phase (i.e., rain, snow, and mixed phase) depends on the size and organization of a weather system. Typically, larger synoptically forced weather systems are better predicted than smaller weather systems, including the associated rain-to-snow transition or freezing-rain areas. Offsets in space or time (i.e., causing misses and false alarms) have a larger effect on the model performance for smaller weather systems. [ABSTRACT FROM AUTHOR]

The quantitative precipitation forecast (QPF) capability of the Variational Doppler Radar Analysis System (VDRAS) is investigated in the Taiwan area, where the complex topography and surrounding oceans pose great challenges to accurate rainfall prediction. Two real cases observed during intensive operation periods (IOPs) 4 and 8 of the 2008 Southwest Monsoon Experiment (SoWMEX) are selected for this study. Experiments are first carried out to explore the sensitivity of the retrieved fields and model forecasts with respect to different background fields. All results after assimilation of the Doppler radar data indicate that the principal kinematic and thermodynamic features recovered by the VDRAS four-dimensional variational data assimilation (4DVAR) technique are rather reasonable. Starting from a background field generated by blending ground-based in situ measurements (radiosonde and surface mesonet station) and reanalysis data over the oceans, VDRAS is capable of capturing the evolution of the major precipitation systems after 2 h of simulation. The model QPF capability is generally comparable to or better than that obtained using only in situ observations or reanalysis data to prepare the background fields. In a second set of experiments, it is proposed to merge the VDRAS analysis field with the Weather Research and Forecasting Model (WRF), and let the latter continue with the following model integration. The results indicate that through this combination, the performance of the model QPF can be further improved. The accuracy of the predicted 2-h accumulated rainfall turns out to be significantly higher than that generated by using VDRAS or WRF alone. This can be attributed to the assimilation of meso- and convective-scale information, embedded in the radar data, into VDRAS, and to better treatment of the topographic effects by the WRF simulation. The results illustrated in this study demonstrate a feasible extension for the application of VDRAS in other regions with similar geographic conditions and observational limitations. [ABSTRACT FROM AUTHOR]

The National Severe Storms Laboratory (NSSL) has developed a hydrometeor classification algorithm (HCA) for use with the polarimetric upgrade of the current Weather Surveillance Radar-1988 Doppler (WSR-88D) network. The algorithm was developed specifically for warm-season convection, but it will run regardless of season, and so its performance on surface precipitation type during winter events is examined here. The HCA output is compared with collocated (in time and space) observations of precipitation type provided by the public. The Peirce skill score (PSS) shows that the NSSL HCA applied to winter surface precipitation displays little skill, with a PSS of only 0.115. Further analysis indicates that HCA failures are strongly linked to the inability of HCA to accommodate refreezing below the first freezing level and to errors in the melting-level detection algorithm. Entrants in the 2009 American Meteorological Society second annual artificial intelligence competition developed classification methods that yield a PSS of 0.35 using a subset of available radar data merged with limited environmental data. Thus, when polarimetric radar data and environmental data are appropriately combined, more information about winter surface precipitation type is available than from either data source alone. [ABSTRACT FROM AUTHOR]

The evolution of the daytime planetary boundary layer (PBL) and its association with warm-season precipitation is strongly impacted by land--atmosphere heat and moisture exchange (hereafter surface exchange). However, substantial uncertainty exists in the parameterization of the surface exchange in numerical weather prediction (NWP) models. In the current study, the authors examine 0--24-h convection-permitting forecasts with different surface exchange strengths for a 6-day period during the International H2O Project (IHOP_2002). Results indicate sensitivity in the timing of simulated afternoon convection initiation and subsequent precipitation amounts to variations in surface exchange strength. Convection initiation in simulations with weak surface exchange was delayed by 2--3 h compared to simulations with strong surface exchange, and area-averaged total precipitation amounts were less by up to a factor of 2. Over the western high plains (105°°--100°°W longitude), where deep convection is locally generated, simulations using a formulation for surface exchange that varied with the vegetation category (height) produced area-averaged diurnal cycles of forecasted precipitation amounts in better agreement with observations than simulations that used the current Advanced Research Weather Research and Forecasting Model (ARW-WRF) formulation. Parcel theory is used to diagnose mechanisms by which differences in surface exchange influence convection initiation in individual case studies. The more rapid initiation in simulations with strong surface exchange results from a more rapid removal of negative buoyancy beneath the level of free convection, which arises primarily from greater PBL warming. [ABSTRACT FROM AUTHOR]

This study assesses the monthly climatology of the timing and placement of convective precipitation events induced by sea and land breezes in the Louisiana--Mississippi--Alabama region, and determines possible reasons for the monthly differences. These objectives were achieved through surface wind climatologies and radar composites from 2003 to 2005, supplemented by statistically significant tests. It is shown that June had an easterly--southeasterly wind regime, whereas July and August featured more southerly flow. These wind regimes may have influenced monthly diurnal wind patterns along the coast. While all months showed a typical pattern of sea-breeze evolution, the land breeze demonstrated monthly variations off the Mississippi and Louisiana coasts. July and August feature a westerly land breeze from Louisiana, while the Mississippi land breeze was stronger in August than in July. A daily wind speed minimum offshore from Mississippi indicates a local transition of influence from the land breeze to the sea breeze, and precedes the peak of the sea breeze at the coastline by about 2 h. During the nighttime hours, radar composites show that precipitation is most prevalent offshore of the Louisiana and Mississippi coastline. By 1100 central daylight time (CDT), precipitation is observed over coastal regions. Local afternoon precipitation is widespread throughout the inland areas, while precipitation is minimal offshore. Statistical significance tests show that areal precipitation coverage (APC) increases in mid- to late summer on the Mississippi coast are due to higher precipitable water and, to a lesser extent, additional CAPE. Greater offshore APC in July and August results from the influence of the local land-breeze setup. Convergence of a land breeze from eastern Louisiana and a stronger land breeze from Mississippi provides the additional lift needed to generate convection over a greater area. [ABSTRACT FROM AUTHOR]

Since 2003 the National Center for Atmospheric Research (NCAR) has been running various experimental convection-allowing configurations of the Weather Research and Forecasting Model (WRF) for domains covering a large portion of the central United States during the warm season (April-July). In this study, the skill of 3-hourly accumulated precipitation forecasts from a large sample of these convection-allowing simulations conducted during 2004-05 and 2007-08 is compared to that from operational North American Mesoscale (NAM) model forecasts using a neighborhood-based equitable threat score (ETS). Separate analyses were conducted for simulations run before and after the implementation in 2007 of positive-definite (PD) moisture transport for the NCAR-WRF simulations. The neighborhood-based ETS (denoted 〈ETS〉 r) relaxes the criteria for 'hits' (i.e., correct forecasts) by considering grid points within a specified radius r. It is shown that 〈ETS〉 r is more useful than the traditional ETS because 〈ETS〉 r can be used to diagnose differences in precipitation forecast skill between different models as a function of spatial scale, whereas the traditional ETS only considers the spatial scale of the verification grid. It was found that differences in 〈ETS〉 r between NCAR-WRF and NAM generally increased with increasing r, with NCAR-WRF having higher scores. Examining time series of 〈ETS〉 r for r = 100 and r = 0 km (which simply reduces to the 'traditional' ETS), statistically significant differences between NCAR-WRF and NAM were found at many forecast lead times for 〈ETS〉100 but only a few times for 〈ETS〉0. Larger and more statistically significant differences occurred with the 2007-08 cases relative to the 2004-05 cases. Because of differences in model configurations and dominant large-scale weather regimes, a more controlled experiment would have been needed to diagnose the reason for the larger differences that occurred with the 2007-08 cases. Finally, a compositing technique was used to diagnose the differences in the spatial distribution of the forecasts. This technique implied westward displacement errors for NAM model forecasts in both sets of cases and in NCAR-WRF model forecasts for the 2007-08 cases. Generally, the results are encouraging because they imply that advantages in convection-allowing relative to convection-parameterizing simulations noted in recent studies are reflected in an objective neighborhood-based metric. [ABSTRACT FROM AUTHOR]

Image warping for spatial forecast verification is applied to the test cases employed by the Spatial Forecast Verification Intercomparison Project (ICP), which includes both real and contrived cases. A larger set of cases is also used to investigate aggregating results for summarizing forecast performance over a long record of forecasts. The technique handles the geometric and perturbed cases with nearly exact precision, as would be expected. A statistic, dubbed here the IWS for image warp statistic, is proposed for ranking multiple forecasts and tested on the perturbed cases. IWS rankings for perturbed and real test cases are found to be sensible and physically interpretable. A powerful result of this study is that the image warp can be employed using a relatively sparse, preset regular grid without having to first identify features. [ABSTRACT FROM AUTHOR]

St. John’s, Newfoundland, Canada (CYYT), is frequently affected by extreme precipitation events, particularly in the cool season (October–April). Previous work classified precipitation events at CYYT into categories by precipitation amount and a manual synoptic typing was performed on the 50 median extreme precipitation events, using two separate methods. Here, consecutive extreme precipitation events in December 2008 are analyzed. These events occurred over a 6-day period and produced over 125 mm of precipitation at CYYT. The first manual typing method, using a backward-trajectory analysis, results in both events being classified as “southwest,” which were previously defined as the majority of the backward trajectories originating in the Gulf of Mexico. The second method of manual synoptic typing finds that the first event is classified as a “cyclone,” while the second is a “frontal” event. A synoptic analysis of both events is conducted, highlighting important dynamic and thermodynamic structures. The first event was characterized by strong quasigeostrophic forcing for ascent in a weakly stable atmosphere in association with a rapidly intensifying extratropical cyclone off the coast of North America and transient high values of subtropical moisture. The second event was characterized by primarily frontogenetical forcing for ascent in a weakly stable atmosphere in the presence of quasi-stationary high values of subtropical moisture, in association with a northeast–southwest-oriented baroclinic zone situated near CYYT. In sum, the synoptic structures responsible for the two events highlight rather disparate means to produce an extreme precipitation event at CYYT. [ABSTRACT FROM AUTHOR]