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This paper proposes a new quantitative precipitation estimation (QPE) technique to provide accurate rainfall estimates in complex terrain, where conventional QPE has limitations. The operational radar QPE in Taiwan is mainly based on the simplified relationship between radar reflectivity Z and rain rate R [R(Z) relation] and only utilizes the single-point lowest available echo to estimate rain rates, leading to low accuracy in complex terrain. Here, we conduct QPE using deep learning that extracts features from 3D radar reflectivities to address the above issues. Convolutional neural networks (CNN) are used to analyze contoured frequency by altitude diagrams (CFADs) to generate the QPE. CNN models are trained on existing rain gauges in northern and eastern Taiwan with the 3-yr data during 2015–17 and validated and tested using 2018 data. The weights of heavy rains (≥10 mm h−1) are increased in the model loss calculation to handle the unbalanced rainfall data and improve accuracy. Results show that the CNN outperforms the R(Z) relation based on the 2018 rain gauge data. Furthermore, this research proposes methods to conduct 2D gridded QPE at every pixel by blending estimates from various trained CNN models. Verification based on independent rain gauges shows that the CNN QPE solves the underestimation of the R(Z) relation in mountainous areas. Case studies are presented to visualize the results, showing that the CNN QPE generates better small-scale rainfall features and more accurate precipitation information. This deep learning QPE technique may be helpful for the disaster prevention of small-scale flash floods in complex terrain. [ABSTRACT FROM AUTHOR]

The Dynamical–Statistical–Analog Ensemble Forecast for Landfalling Typhoon Daily Precipitation (DSAEF_LTP_D) model is introduced in this paper. To improve the DSAEF_LTP_D model's forecasting ability, tropical cyclone (TC) translation speed was introduced. Taking Supertyphoon Lekima (2019), which produced widespread heavy rainfall from 9 to 11 August 2019 as the target TC, two simulation experiments associated with the prediction of daily precipitation were conducted: the first involving the DSAEF_LTP_D model containing only the TC track (the actual trajectory of the TC center), named DSAEF_LTP_D-1; and the second containing both TC track and translation speed, named DSAEF_LTP_D-2. The results show the following: 1) With TC translation speed added into the model, the forecasting performance for heavy rainfall (24-h accumulated precipitation exceeding 50 and 100 mm) on 9 and 10 August improves, being able to successfully capture the center of heavy rainfall, but the forecasting performance is the same as DSAEF_LTP_D-1 on 11 August. 2) Compared with four numerical weather prediction (NWP) models (i.e., ECMWF, GFS, GRAPES, and SMS-WARMS), the TS100 + TS50 (the sum of TS values for predicting 24-h accumulated precipitation of ≥100 and ≥50 mm) of DSAEF_LTP_D-2 is comparable to the best performer of the NWP models (ECMWF) on 9 and 10 August, while the performance of DSAEF_LTP_D model for predicting heavy rainfall on 11 August is poor. 3) The newly added similarity regions make up for the deficiency that the similarity regions are narrower when the TC track is northward, which leads to DSAEF_LTP_D-2 having a better forecasting performance for heavy rainfall on 11 August, with the TS100 + TS50 increasing from 0.3021 to 0.4286, an increase of 41.87%. [ABSTRACT FROM AUTHOR]

Machine learning algorithms are trained on a 10-yr archive of composite weather radar images in the Swiss Alps to nowcast precipitation growth and decay in the next few hours in moving coordinates (Lagrangian frame). The hypothesis of this study is that growth and decay is more predictable in mountainous regions, which represent a potential source of practical predictability by machine learning methods. In this paper, artificial neural networks (ANN) are employed to learn the complex nonlinear dependence relating the growth and decay to the input predictors, which are geographical location, mesoscale motion vectors, freezing level height, and time of the day. The average long-term growth and decay patterns are effectively reproduced by the ANN, which allows exploring their climatology for any combination of predictors. Due to the low intrinsic predictability of growth and decay, its prediction in real time is more challenging, but is substantially improved when adding persistence information to the predictors, more precisely the growth and decay and precipitation intensity in the immediate past. The improvement is considerable in mountainous regions, where, depending on flow direction, the root-mean-square error of ANN predictions can be 20%–30% lower compared with persistence. Because large uncertainty is associated with precipitation forecasting, deterministic machine learning predictions should be coupled with a model for the predictive uncertainty. Therefore, we consider a probabilistic perspective by estimating prediction intervals based on a combination of quantile decision trees and ANNs. The probabilistic framework is an attempt to address the problem of conditional bias, which often characterizes deterministic machine learning predictions obtained by error minimization. [ABSTRACT FROM AUTHOR]

Freezing rain can cause severe impacts, particularly when it persists for many hours. In this paper, we present the climatology of long-duration (6 or more hours) freezing rain events in the United States and Canada from 1979 to 2016. We identify three focus regions from this climatology and examine the archetypal thermodynamic evolution of events in each region using surface and radiosonde observations. Long-duration events occur most frequently in the northeastern United States and southeastern Canada, where freezing rain typically begins as lower-tropospheric warm-air advection develops the warm layer aloft. This warm-air advection and the latent heat of fusion released when rain freezes at the surface erode the cold layer, and freezing rain transitions to rain once the surface temperature reaches 0°C. In the southeastern United States, a larger percentage of events are of long duration than elsewhere in North America. Weak surface cold-air advection and evaporative cooling in the particularly dry onset cold layers there prevent surface temperatures from rising substantially during events. Finally, the south-central United States has a regional maximum in the occurrence of the top 1% of events by duration (18 or more hours), despite the relative rarity of freezing rain there. These events are associated with particularly warm/deep onset warm layers, with persistent low-level cold-air advection maintaining the cold layer. The thermodynamic evolutions we have identified highlight characteristics that are key to supporting persistent freezing rain in each region and may warrant particular attention from forecasters tasked with predicting these events. [ABSTRACT FROM AUTHOR]

Understanding and forecasting tropical cyclone (TC) intensity change continues to be a paramount challenge for the research and operational communities, partly because of inherent systematic biases contained in model guidance, which can be difficult to diagnose. The purpose of this paper is to present a method to identify such systematic biases by comparing forecasts characterized by large intensity errors with analog forecasts that exhibit small intensity errors. The methodology is applied to the 2015 version of the Hurricane Weather Research and Forecasting (HWRF) Model retrospective forecasts in the North Atlantic (NATL) and eastern North Pacific (EPAC) basins during 2011-14. Forecasts with large 24-h intensity errors are defined to be in the top 15% of all cases in the distribution that underforecast intensity. These forecasts are compared to analog forecasts taken from the bottom 50% of the error distribution. Analog forecasts are identified by finding the case that has 0-24-h intensity and wind shear magnitude time series that are similar to the large intensity error forecasts. Composite differences of the large and small intensity error forecasts reveal that the EPAC large error forecasts have weaker reflectivity and vertical motion near the TC inner core from 3 h onward. Results over the NATL are less clear, with the significant differences between the large and small error forecasts occurring radially outward from the TC core. Though applied to TCs, this analog methodology could be useful for diagnosing systematic model biases in other applications. [ABSTRACT FROM AUTHOR]

This paper explores the skill of river stage forecasts produced by the National Weather Service (NWS). Despite the importance of the verification process in establishing a reference that allows advancement in river forecast technology, there is relatively little literature on this topic. This study aims to contribute to this subject. The study analyzed the North Central River Forecast Center's river stage forecasts for 51 gauges in eastern and central Iowa between 1999 and 2014. The authors explored forecast skill dependence characteristics such as upstream area, water travel time, and the number of gauges located upstream of each forecasting point. They also assessed the influence of rainfall uncertainty on stage error by examining the relationship between the forecast skill and its antecedent 24-h observed rainfall. The results show that when using persistence as a reference for comparison with NWS actual forecasts, the NWS forecasts are better for predictions below and above flood stage. The difference in root-mean-square error (RMSE) between the actual and persistence forecasts ranges between 0.04 and 1.24 ft, and it increases with lead time. Locations with fewer upstream gauges exhibit greater variation in forecast skill than locations that are well gauged, especially at high flood levels. Strong predictive relationships between the physical characteristics of a basin (travel time, upstream drainage area), rainfall quantities, and forecast skill have not been identified. [ABSTRACT FROM AUTHOR]

Mathematical derivation, meteorological justification, and comparison to model direct precipitation forecasts are the three main concerns recently raised by Schultz and Spengler about moist divergence (MD) and moist vorticity (MV), which were introduced in earlier work by Qian et al. That previous work demonstrated that MD (MV) can in principle be derived mathematically with a value-added empirical modification. MD (MV) has a solid meteorological basis. It combines ascent motion and high moisture: the two elements necessary for rainfall. However, precipitation efficiency is not considered in MD (MV). Given the omission of an advection term in the mathematical derivation and the lack of precipitation efficiency, MD (MV) might be suitable mainly for heavy rain events with large areal coverage and long duration caused by large-scale quasi-stationary weather systems, but not for local intense heavy rain events caused by small-scale convection. In addition, MD (MV) is not capable of describing precipitation intensity. MD (MV) worked reasonably well in predicting heavy rain locations from short to medium ranges as compared with the ECMWF model precipitation forecasts. MD (MV) was generally worse than (though sometimes similar to) the model heavy rain forecast at shorter ranges (about a week) but became comparable or even better at longer ranges (around 10 days). It should be reiterated that MD (MV) is not intended to be a primary tool for predicting heavy rain areas, especially in the short range, but is a useful parameter for calibrating model heavy precipitation forecasts, as stated in the original paper. [ABSTRACT FROM AUTHOR]

Part I of this two-part paper examined the satellite-derived rainfall accumulation and rain potential history of Hurricanes Isidore and Lili (2002). This paper (Part II) uses analyses from the Navy Operational Global Atmospheric Prediction System (NOGAPS) to examine the water budget and environmental parameters and their relationship to the precipitation for these two storms. Factors other than storm size are found to account for large volumetric differences in storm total rainfall between Lili and Isidore. It is found that the horizontal moisture convergence was crucial to the initiation and maintenance of Isidore’s intense rainfall before and during its landfall. When the storm was over the ocean, the ocean moisture flux (evaporation) was the second dominant term among the moisture sources that contribute to precipitation. During Isidore’s life history, the strong horizontal moisture flux convergence corresponded to the large storm total precipitable water. The large difference in budget-derived stored cloud ice and liquid water between Isidore and Lili is corroborated from Tropical Rainfall Measuring Mission (TRMM) measurements. During Isidore’s landfall, the decrease in environmental water vapor contributed to rainfall in a very small amount. These results indicate the importance of the environmental precipitable water and moisture convergence and ocean surface moisture flux in generating Isidore’s large rainfall volume and inland flooding as compared with Lili’s water budget history. Both the moisture convergence and ocean flux were small for Lili. [ABSTRACT FROM AUTHOR]

Inland flooding caused by heavy rainfall from landfalling tropical cyclones is a significant threat to life and property. The tropical rainfall potential (TRaP) technique, which couples satellite estimates of rain rate in tropical cyclones with track forecasts to produce a forecast of 24-h rainfall from a storm, was developed to better estimate the magnitude of this threat. This paper outlines the history of the TRaP technique, details its current algorithms, and offers examples of its use in forecasting. Part II of this paper covers verification of the technique. [ABSTRACT FROM AUTHOR]

Although the use of anomaly fields in the forecast process has been shown to be useful and has caught forecasters' attention, current short-range (1-3 days) weather analyses and forecasts are still predominantly total-field based. This paper systematically examines the pros and cons of anomaly- versus total-field-based approaches in weather analysis using a case from 1 July 1991 (showcase) and 41 cases from 1998 (statistics) of heavy rain events that occurred in China. The comparison is done for both basic atmospheric variables (height, temperature, wind, and humidity) and diagnostic parameters (divergence, vorticity, and potential vorticity). Generally, anomaly fields show a more enhanced and concentrated signal (pattern) directly related to surface anomalous weather events, while total fields can obscure the visualization of anomalous features due to the climatic background. The advantage is noticeable in basic atmospheric variables, but is marginal in nonconservative diagnostic parameters and is lost in conservative diagnostic parameters. Sometimes a mix of total and anomaly fields works the best; for example, in the moist vorticity when anomalous vorticity combines with total moisture, it can depict the heavy rain area the best when comparing to either the purely total or purely anomalous moist vorticity. Based on this study, it is recommended that anomaly-based weather analysis could be a valuable supplement to the commonly used total-field-based approach. Anomalies can help a forecaster to more quickly identify where an abnormal weather event might occur as well as more easily pinpoint possible meteorological causes than a total field. However, one should not use the anomaly structure approach alone to explain the underlying dynamics without a total field. [ABSTRACT FROM AUTHOR]

The early season rainfall (ESR) over southern China, usually occurring from April to June, is a prominent meteorological phenomenon of the East Asian monsoon system. In this paper, output from the 45-day hindcast by the NCEP Climate Forecast System, version 2 (CFSv2), and various observational datasets are analyzed to assess the predictability of the ESR and associated atmospheric circulation. Results show that CFSv2 can successfully predict the ESR and associated circulation patterns over southern China. The lower-tropospheric convergence and upper-tropospheric divergence as well as the local upward motion over southern China lead to the formation of ESR. Analysis of bias shows small differences and close relationships between the predicted and observed ESR values when the forecast lead time is less than 2 weeks. The skill in the ESR predictions by CFSv2 decreases significantly when the lead time is longer than 2 weeks. Overall, CFSv2 has a higher level of skill when predicting the southern China ESR compared to the rainfall over other Asian regions during the same period of time. [ABSTRACT FROM AUTHOR]

Studies have found that convective storms with overshooting-top (OT) signatures in weather satellite imagery are often associated with hazardous weather, such as heavy rainfall, tornadoes, damaging winds, and large hail. An objective satellite-based OT detection product has been developed using 11- μm infrared window (IRW) channel brightness temperatures (BTs) for the upcoming R series of the Geostationary Operational Environmental Satellite (GOES-R) Advanced Baseline Imager. In this study, this method is applied to GOES-12 IRW data and the OT detections are compared with radar data, severe storm reports, and severe weather warnings over the eastern United States. The goals of this study are to 1) improve forecaster understanding of satellite OT signatures relative to commonly available radar products, 2) assess OT detection product accuracy, and 3) evaluate the utility of an OT detection product for diagnosing hazardous convective storms. The coevolution of radar-derived products and satellite OT signatures indicates that an OT often corresponds with the highest radar echo top and reflectivity maximum aloft. Validation of OT detections relative to composite reflectivity indicates an algorithm false-alarm ratio of 16%, with OTs within the coldest IRW BT range (<200 K) being the most accurate. A significant IRW BT minimum typically present with an OT is more often associated with heavy precipitation than a region with a spatially uniform BT. Severe weather was often associated with OT detections during the warm season (April-September) and over the southern United States. The severe weather to OT relationship increased by 15% when GOES operated in rapid-scan mode, showing the importance of high temporal resolution for observing and detecting rapidly evolving cloud-top features. Comparison of the earliest OT detection associated with a severe weather report showed that 75% of the cases occur before severe weather and that 42% of collocated severe weather reports had either an OT detected before a severe weather warning or no warning issued at all. The relationships between satellite OT signatures, severe weather, and heavy rainfall shown in this paper suggest that 1) when an OT is detected, the particular storm is likely producing heavy rainfall and/or possibly severe weather; 2) an objective OT detection product can be used to increase situational awareness and forecaster confidence that a given storm is severe; and 3) this product may be particularly useful in regions with insufficient radar coverage. [ABSTRACT FROM AUTHOR]

An improved velocity dealiasing algorithm is developed as an extension of the Next Generation Weather Radar (NEXRAD) dealiasing algorithm. The algorithm described in this paper is evaluated on selected China Next Generation Weather Radar (CINRAD) S-band radar radial velocity observations. This algorithm includes four modules for removing weak signals and determining the starting radial as a prelude to identifying and correcting aliased velocities. The proposed dealiasing algorithm was tested on 14 different weather systems, composed of typhoons, squall lines, and heavy rains. The results show that the algorithm is robust and stable for dealiasing S-band CINRAD radial velocity measurements. The performance levels for the typhoon and heavy rain cases are slightly better than for squall-line cases. [ABSTRACT FROM AUTHOR]

This paper examines the sensitivity of short-term forecasts of the western North Pacific subtropical high (WNPSH) and rainfall to sea surface temperature (SST) uncertainty using the Coupled Ocean--Atmosphere Mesoscale Prediction System (COAMPS). A comparison of analyzed SSTs with satellite observations of SST indicates that SST analysis errors are particularly pronounced on horizontal scales from 100 to 200 km, similar to the mesoscale eddy scales in the Kuroshio region. Since significant oceanic variations occur on these scales, it is of interest to examine the effects of representing this small-scale uncertainty with random, scale-dependent perturbations. An SST ensemble perturbation generation technique is used here that enables temporal and spatial correlations to be controlled and produces initial SST fields comparable to satellite observations. The atmospheric model develops large uncertainty in the Korea and Japan area due to the fluctuation in the horizontal pressure gradient caused by the location of the WNPSH. This, in turn, increases the variance of the low-level jet (LLJ) over southeast China, resulting in large differences in the moist transport flux from the tropical ocean and subsequent rainfall. Validation using bin-mean statistics shows that the ensemble forecast with the perturbed SST better distinguishes large forecast error variance from small forecast error variance. The results suggest that using the SST perturbation as a proxy for the ocean ensemble in a coupled atmosphere and ocean ensemble system is feasible and computationally efficient. [ABSTRACT FROM AUTHOR]

Significant cool season precipitation along the western coast of North America is often associated with intense water vapor transport (IWVT) from the Pacific Ocean during favorable synoptic-scale flow regimes. These relatively narrow and intense regions of water vapor transport can originate in either the tropical or subtropical oceans, and sometimes have been referred to as Pineapple Express events in previous literature when originating near Hawaii. However, the focus of this paper will be on diagnosing the synoptic-scale signatures of all significant water vapor transport events associated with poleward moisture transport impacting the western coast of Canada, regardless of the exact points of origin of the associated atmospheric river. A trajectory analysis is used to partition the events as a means of creating coherent and meaningful synoptic-scale composites. The results indicate that these IWVT events can be clustered by the general area of origin of the majority of the saturated parcels impacting British Columbia and the Yukon Territories. IWVT events associated with more zonal trajectories are characterized by a strong and mature Aleutian low, whereas IWVT events associated with more meridional trajectories are often characterized by an anticyclone situated along the California or Oregon coastline, and a relatively mature poleward-traveling cyclone, commonly originating in the central North Pacific. [ABSTRACT FROM AUTHOR]

This paper investigates the response of the land surface and the lowest section of the atmospheric surface layer to rainfall events and through the subsequent drying out period. The impacts of these sequences of rainfall and drying events in controlling near-surface temperatures are put into the context of malaria transmission modeling using temperature controls on the survivability of mosquitoes that are developing the malaria parasite. Observations using measurements from a dwelling hut, constructed to a local design at Wankama near Niamey, Niger, show that as the atmosphere gets moister and colder following rainfall, there is a potentially higher risk of malaria transmission during the rainy days. As the atmosphere gets warmer and drier during the drying period, there is a potentially decreasing rate of malaria transmission as the increasing temperature reduces the survivability of the mosquitoes. A numerical weather prediction model comparison shows that the high-resolution limited-area model outperforms the global-scale model and shows good agreement with the observations. Statistical analysis from the model results confirms that the findings are not restricted to a single location or single time of the day. It was also found that air temperatures over forest areas do not change as much during the study period, since the longer memory of the soil moisture means there is relatively little influence from single rainfall events. [ABSTRACT FROM AUTHOR]

High-resolution forecasts may be quite useful even when they do not match the observations exactly. Neighborhood verification is a strategy for evaluating the “closeness” of the forecast to the observations within space–time neighborhoods rather than at the grid scale. Various properties of the forecast within a neighborhood can be assessed for similarity to the observations, including the mean value, fractional coverage, occurrence of a forecast event sufficiently near an observed event, and so on. By varying the sizes of the neighborhoods, it is possible to determine the scales for which the forecast has sufficient skill for a particular application. Several neighborhood verification methods have been proposed in the literature in the last decade. This paper examines four such methods in detail for idealized and real high-resolution precipitation forecasts, highlighting what can be learned from each of the methods. When applied to idealized and real precipitation forecasts from the Spatial Verification Methods Intercomparison Project, all four methods showed improved forecast performance for neighborhood sizes larger than grid scale, with the optimal scale for each method varying as a function of rainfall intensity. [ABSTRACT FROM AUTHOR]

The principal source of information for operational flash flood monitoring and warning issuance is weather radar–based quantitative estimates of precipitation. Rain gauges are considered truth for the purposes of validating and calibrating real-time radar-derived precipitation data, both in a real-time sense and climatologically. This paper examines various uncertainties and challenges involved with using radar and rain gauge data in a severe local storm environment. A series of severe thunderstorm systems that occurred across northeastern Montana illustrates various problems with comparing radar precipitation estimates and real-time gauge data, including extreme wind effects, hail, missing gauge data, and radar quality control. Ten radar–gauge time series pairs were analyzed with most found to be not useful for real-time radar calibration. These issues must be carefully considered within the context of ongoing efforts to develop robust real-time tools for evaluating radar–gauge uncertainties. Recommendations are made for radar and gauge data quality control efforts that would benefit the operational use of gauge data. [ABSTRACT FROM AUTHOR]

In this paper, the three-dimensional variational data assimilation scheme (3DVAR) in the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (Penn State–NCAR) Mesoscale Model (MM5) is used to study the impact of assimilating Atmospheric Infrared Sounder (AIRS) retrieved temperature and moisture profiles on board Aqua, a satellite that is part of NASA’s Earth Observing System. A record-breaking heavy rain event that occurred over Mumbai, India, on 26 July 2005 with 24-h rainfall exceeding 94 cm was used for the simulation. By analyzing the data from the NCEP–NCAR reanalysis, possible causes of this heavy rainfall event were investigated. The temporal evolution of meteorological fields clearly indicates the formation of midtropospheric mesoscale vortices over Mumbai that exactly coincides with the duration of the intense rainfall. Analysis also indicated the midlevel dryness with higher temperature and moisture in the lower levels. This midlevel dryness with high temperature and moisture in the lower levels increases the conditional instability, which was conducive for the development of very severe local thunderstorms. The midtropospheric mesoscale vortices existed over Mumbai together with lower-level instability and the active monsoon conditions over the west coast resulted in intense rainfall, on the order of 94 cm in 24 h. Numerical experiments were conducted, with two nested domains (45- and 15-km grid spacing). The assimilation of the AIRS-retrieved temperature and moisture profiles produced significant impacts on the location and intensity of the simulated rainfall. It is seen from the numerical experiments that the assimilation of AIRS data could produce the structure of mesoscale vortices, and lower-level thermodynamics and convergence much more realistically compared with the control simulation. The spatial distribution of the rainfall from the simulation using AIRS data was more realistic than that without AIRS data. To make the quantitative comparison of the predicted rainfall with the observed one, the equitable threat score and bias were calculated for different threshold values of rainfall. Inclusion of AIRS data significantly improved the precipitation as indicated by the equitable threat scores and biases for almost all of the threshold rainfall categories. [ABSTRACT FROM AUTHOR]

Extreme rainfall events contribute a large portion of wintertime precipitation to northern California. The motivations of this paper were to study the observed differences in the patterns between extreme and more commonly occurring lighter rainfall events, and to study whether anomaly fields might be used to discriminate between them. Daily (1200–1200 UTC) precipitation amounts were binned into three progressively heavier categories (12.5–50.0 mm, light; 50–100 mm, moderate; and >100 mm, heavy) in order to help identify the physical processes responsible for extreme precipitation in the Sierra Nevada range between 37.5° and 41.0°N. The composite fields revealed marked differences between the synoptic patterns associated with the three different groups. The heavy composites showed a much stronger, larger-scale, and slower-moving negative geopotential height anomaly off the Pacific coast of Oregon and Washington than was revealed in either of the other two composites. The heavy rainfall events were also typically associated with an atmospheric river with anomalously high precipitable water (PW) and 850-hPa moisture flux (MF) within it. The standardized PW and MF anomalies associated with the heavy grouping were higher and were slower moving than in either of the lighter bins. Three multiday heavy rainfall events were closely examined in order to ascertain whether anomaly patterns could provide forecast utility. Each of the multiday extreme rainfall events investigated was associated with atmospheric rivers that contained highly anomalous 850-hPa MF and PW within it. Each case was also associated with an unusually intense negative geopotential height anomaly that was similarly located off of the west coast of the United States. The similarities in the anomaly pattern among the three multiday extreme events suggest that standardized anomalies might be useful in predicting extreme multiday rainfall events in the northern Sierra range. [ABSTRACT FROM AUTHOR]

This paper presents an evaluation study of a real-time fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5) mesoscale ensemble prediction system in the Taiwan area during the 2003 mei-yu season. The ensemble system consists of 16 members that used the same nested domains of 45- and 15-km resolutions, but different model settings of the initial conditions (ICs), the cumulus parameterization scheme (CPS), and the microphysics scheme (MS). Verification of geopotential height, temperature, relative humidity, and winds in the 15-km grid shows that the members using the Kain–Fritsch CPS performed better than those using the Grell CPS, and those using the Central Weather Bureau (CWB) Nonhydrostatic Forecast System (NFS) ICs fared better than those using the CWB Global Forecast System (GFS) ICs. The members applying the mixed-phase MS generally exhibited the smallest errors among the four MSs. Precipitation verification shows that the members using the Grell CPS, in general, had higher equitable threat scores (ETSs) than those using the Kain–Fritsch CPS, that the members with the GFS ICs performed better than with the NFS ICs, and that the mixed-phase and Goddard MSs gave relatively high ETSs in the rainfall simulation. The bias scores show that, overall, all 16 members underforecasted rainfall. Comparisons of the ensemble means show that, on average, an ensemble mean, no matter how many members it contains, can produce better forecasts than an individual member. Among the three possible elements (IC, CPS, and MS) that can be varied to compose an ensemble, the ensemble that contains members with all three elements varying performed the best, while that with two elements varying was second best, and that with only one varying was the worst. Furthermore, the first choice for composing an ensemble is to use perturbed ICs, followed by the perturbed CPS, and then the perturbed MS. [ABSTRACT FROM AUTHOR]

Rain-rate retrievals from passive microwave sensors are useful for a number of applications related to weather forecasting. For example, in the United States, such estimates are useful for offshore rainfall systems approaching land and in regions where the Weather Surveillance Radar-1988 Doppler (WSR-88D) network is inadequate. Improvements have been made to the rain-rate retrieval from the Advanced Microwave Sounding Unit (AMSU) on board the National Oceanic and Atmospheric Administration (NOAA) Polar Orbiting Environmental Satellites (POESs). The new features of the improved rain-rate algorithm include a two-stream correction of the satellite brightness temperatures at 89 and 150 GHz, cloud- and rain-type classification for better retrieval of rain rate, and removal of the two ad hoc thresholds in the ice water path (IWP) and effective diameter (De) retrieval where the scattering signals are very small. In this paper, the new algorithm has been compared to the previous NOAA operational algorithm. In particular, the better utilization of the measurements at and above 150 GHz is shown to produce improved sensitivity to light rainfall associated with winter season storm systems. This improvement is demonstrated through a wintertime case study over southern California during February 2003. [ABSTRACT FROM AUTHOR]

The impact of in situ rawinsonde (raob) data, remotely sensed Geostationary Operational Environmental Satellite (GOES), and Polar Operational Environmental Satellite (POES) data routinely used in NCEP’s Eta Data Assimilation/Forecast System (EDAS) is studied for extended-length time periods during four seasons. The work described in this paper is relevant for users of the Eta Model trying to compare and contrast the overall forecast impact of traditional, mostly land-based rawinsonde data with remotely sensed data that are available domainwide. The case studies chosen consist of 15-day periods during fall 2001, winter 2001/02, spring 2002, and summer 2002. During these periods, a 32-km/60-layer November 2001 version of the EDAS is run four times at both 0000 and 1200 UTC. The four runs include a control run, which utilizes all data types routinely used in the EDAS, and three experimental runs in which either all rawinsonde, GOES, or POES data are denied. Differences between the experimental and control runs are then accumulated over the 15-day periods and analyzed to demonstrate the 24- and 48-h forecast impact of these data types in the EDAS. Conventional meteorological terms evaluated include mean sea level pressure as well as temperature, both components of the wind, and relative humidity. Comparisons are made on seven pressure levels extending from near the earth’s surface to the lower stratosphere. The diagnostics are computed over both the entire horizontal model domain, and within a subsection covering the continental United States and adjacent coastal waters (extended CONUS). The 24-h domainwide results show that a positive forecast impact is achieved from all three data sources during all four seasons. Cumulatively, the rawinsonde data have the largest positive impact over both the entire model domain and extended CONUS. However, GOES data have the largest contribution for several fields, especially moisture during summer and fall 2001. In general, GOES data also provide larger forecast impacts than POES data, especially for the wind components. All three data types demonstrate comparable forecast impact in terms of relative humidity. Finally, raob and POES data display a “spike” in positive forecast impact in the lower stratosphere during three of the four seasons. Two additional findings from this study are also important. The first is that the forecast impact of all data types drops by at least a factor of 2 during all seasons between 24 and 48 h. The second is that GOES data show a preference for providing nearly equal improvement to the 0000 and 1200 UTC forecast cycles, while rawinsonde and especially POES data provide consistently larger forecast impacts at 1200 than at 0000 UTC. [ABSTRACT FROM AUTHOR]

An updated statistical scheme for forecasting seasonal tropical cyclone activity in the Atlantic basin by 1 December of the previous year is presented. Previous research by Gray and colleagues at Colorado State University showed that a statistical forecast issued on 1 December of the previous year could explain up to about 50% of the jackknife hindcast variance for the 1950–90 time period. Predictors utilized in the original forecast scheme included a forward extrapolation of the quasi-biennial oscillation (QBO) and two measures of West African rainfall. This forecast has been issued since 1991 but has shown little skill because of the as yet unexplained failure of the West African rainfall predictors during the 1990s. The updated scheme presented in this paper does not utilize West African rainfall predictors. It employs the new NCEP–NCAR reanalysis data and involves predictors that span the globe. Much experimentation has led to the choosing of conditions associated with the El Niño-Southern Oscillation (ENSO), the Arctic Oscillation (AO), the North Atlantic Oscillation (NAO), the Pacific–North American pattern (PNA), and the QBO. This new statistical scheme shows similar cross-validated (jackknifed) hindcast skill to the original scheme (explaining up to about 50% of the variance) but is developed over a decade longer time period (1950–2001). In addition, since all predictors are taken directly from the NCEP–NCAR reanalysis, one does not need to consider rainfall collection issues that have caused major problems with the West African rainfall data since 1995. Hypothetical physical linkages between the predictors and the following year's hurricane activity are also presented. Based on the net tropical cyclone (NTC) activity prediction and a weighted average of North Atlantic sea surface temperatures, forecasts of U.S. hurricane landfall probability that showed considerable hindcast skill over the 52-yr period of 1950–2001 can also be issued. [ABSTRACT FROM AUTHOR]

This paper presents results of numerical experiments on five precipitating events: two cases of convective systems over the northern French plains that induced localized flooding, two cases of quasi-stationary systems over the southern French mountainous areas with recorded precipitation above 100 mm in less than 3-4 h, and the extreme flooding case of 12-13 November 1999 with daily precipitation having reached 550 mm, which led to more than 30 deaths. The ability of a high-resolution (2.5 km) model to forecast the quantitative precipitation associated with these five events is evaluated. Both qualitative evaluations that compare observed and modeled reflectivities or surface precipitation, and quantitative evaluations based on classical scores are performed to assess the quality of the numerical experiments. Starting from the same analysis as the operational models, the high-resolution model improves the results for one of the cases, the extreme flash flood case. For the four other cases, higher resolution and more advanced physics than those currently used in operational models do not improve the results. In order to verify that the failures of the high-resolution simulations are due to the initial conditions, the initial state of the simulations are modified for these four cases by using more mesoscale observations, such as mesonet surface observations, radar reflectivities, and Meteosat data. With these modified initial conditions, the high-resolution model produces more realistic precipitation fields. The description of the humidity field in the initial state is crucial for the forecast of the convective systems. Information, such as the presence of a mid- to upper-tropospheric saturated area associated with the developing convective clouds, or nearly saturated low levels, is essential. [ABSTRACT FROM AUTHOR]

This paper reports on an assessment of quantitative precipitation forecasts (QPFs) issued by the Arkansas-Red Basin River Forecast Center during the period May-June 1995. Assessment procedures involved spatial statistical procedures applied to the QPFs and Stage III calibrated radar-rainfall fields, and overlays of these. The QPF estimates were broad regional estimates that corresponded reasonably well to actual storm characteristics over the region. Over smaller areas on the order of 104570360f the region (∼9000 mi[SUP2]) the character of actual storms departs significantly from the QPF. Embedded storm cells having high intensity rainfall were not well predicted by the QPF. There was indication that QPF skill increased with rainfall magnitude. [ABSTRACT FROM AUTHOR]