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The ability to predict heavy rain and floods in Africa is urgently needed to reduce the socioeconomic costs of these events and increase resilience as climate changes. Numerical weather prediction in this region is challenging, and attention is being drawn to observationally based methods of providing short-term nowcasts (up to ∼6-h lead time). In this paper a freely available nowcasting package, pySTEPS, is used to assess the potential to provide nowcasts of satellite-derived convective rain rate for West Africa. By analyzing a large number of nowcasts, we demonstrate that a simple approach of "optical flow" can have useful skill at 2-h lead time on a 10-km scale and 4-h lead time at larger scales (200 km). A diurnal variation in nowcast skill is observed, with the worst-performing nowcasts being those that are initialized at 1500 UTC. Comparison with existing nowcasts is presented. Such nowcasts, if implemented operationally, would be expected to have significant benefits. Significance Statement: A freely available, easy-to-use nowcasting package has been applied to satellite-retrieved rainfall rates for West Africa, and extrapolations have useful skill at up to 4 h of lead time. [ABSTRACT FROM AUTHOR]

This paper describes a forecasting configuration of the Geophysical Fluid Dynamics Laboratory (GFDL) High-resolution Atmospheric Model (HiRAM). HiRAM represents an early attempt in unifying, within a global modeling framework, the capabilities of GFDL's low-resolution climate models for Intergovernmental Panel on Climate Change (IPCC) type climate change assessments and high-resolution limited-area models for hurricane predictions. In this study, the potential of HiRAM as a forecasting tool is investigated by applying the model to the near-term and intraseasonal hindcasting of tropical cyclones (TCs) in the Atlantic basin from 2006 to 2009. Results demonstrate that HiRAM provides skillful near-term forecasts of TC track and intensity relative to their respective benchmarks from t == 48 h through t == 144 h. At the intraseasonal time scale, a simple HiRAM ensemble provides skillful forecasts of 21-day Atlantic basin TC activity at a 2-day lead time. It should be noted that the methodology used to produce these hindcasts is applicable in a real-time forecasting scenario. While the initial experimental results appear promising, the HiRAM forecasting system requires various improvements in order to be useful in an operational setting. These modifications are currently under development and include a data assimilation system for forecast initialization, increased horizontal resolution to better resolve the vortex structure, 3D ocean model coupling, and wave model coupling. An overview of these ongoing developments is provided, and the specifics of each will be described in subsequent papers. [ABSTRACT FROM AUTHOR]

The performance of summer precipitation prediction skill of the Beijing Climate Center Climate System Model (BCC_CSM1.1; hereafter CSM) over the East Asia-west Pacific (EA-WP) region indicates the need for further development in order to improve model prediction skill. Two methods, namely the statistical alone prediction and the statistical and model combined prediction, are proposed to improve prediction skill. For the former, more than 2000 combinations of precursory predictors are considered in the predictive model; both most similar and dissimilar information are considered in the prediction process. For the latter, both the statistical analog information and the CSM's forecast are considered in the predictive model; this new prediction method is based on merging information from both a dynamical model and historical analogs. Cross validation of summer precipitation over EA-WP for the years 1991-2010 shows that the average spatial anomaly correlation coefficient (ACC) of the statistical alone prediction is 0.16 and the average spatial root-mean-standard error (RMSE) is 22.7, which are much improved compared to the systematic error correction with ACC and RMSE being 0.09 and 25.0 respectively. The ACC and RMSE results from the statistical and CSM combined prediction are 0.22 and 21.8, respectively, which also indicates an improvement compare to the statistical alone prediction. Independent sample validation for the years 2011-13 indicates that the average ACCs of the statistical alone prediction and the statistical and CSM combined prediction (0.25 and 0.24) are enhanced compared to the systematic error correction (−0.02). The average RMSE is also improved from 25.0 to 23.6 and 22.7. Therefore, the two new prediction methods proposed in this paper both demonstrate the possibilities for the operational prediction of summer precipitation over EA-WP. [ABSTRACT FROM AUTHOR]

Accurately forecasting snowfall is a challenge. In particular, one poorly understood component of snowfall forecasting is determining the snow ratio. The snow ratio is the ratio of snowfall to liquid equivalent and is inversely proportional to the snow density. In a previous paper, an artificial neural network was developed to predict snow ratios probabilistically in three classes: heavy (1:1 < ratio < 9:1), average (9:1 ≤ ratio ≤ 15:1), and light (ratio > 15:1). A Web-based application for the probabilistic prediction of snow ratio in these three classes based on operational forecast model soundings and the neural network is now available. The goal of this paper is to explore the statistical characteristics of the snow ratio to determine how temperature, liquid equivalent, and wind speed can be used to provide additional guidance (quantitative, wherever possible) for forecasting snowfall, especially for extreme values of snow ratio. Snow ratio tends to increase as the low-level (surface to roughly 850 mb) temperature decreases. For example, mean low-level temperatures greater than -2.7°C rarely (less than 5% of the time) produce snow ratios greater than 25:1, whereas mean low-level temperatures less than -10.1°C rarely produce snow ratios less than 10:1. Snow ratio tends to increase strongly as the liquid equivalent decreases, leading to a nomogram for probabilistic forecasting snowfall, given a forecasted value of liquid equivalent. For example, liquid equivalent amounts 2.8–4.1 mm (0.11–0.16 in.) rarely produce snow ratios less than 14:1, and liquid equivalent amounts greater than 11.2 mm (0.44 in.) rarely produce snow ratios greater than 26:1. The surface wind speed plays a minor role by decreasing snow ratio with increasing wind speed. Although previous research has shown simple relationships to determine the snow ratio are difficult to obtain, this note helps to clarify some situations where such relationships are possible. [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 capability to accurately analyze the spatial distribution of temperature and wind at very high spatial (2.5-1 km) and temporal (60-5 min) resolutions is of interest in many modern techniques (e.g., nowcasting and statistical downscaling). In addition to observational data, the generation of such analyses requires background information to adequately resolve nonstatic, small-scale phenomena. Numerical weather prediction (NWP) models are of continuously increasing skill and are more capable of providing valuable information on convection-resolving scales. The present paper discusses the impact of two operational NWP models on hourly 2-m temperature and 10-m wind analyses as created by the Integrated Nowcasting through Comprehensive Analysis (INCA) system, which includes a topographic downscaling procedure. The NWP models used for this study are a revised version of ARPEGE-ALADIN (ALARO; 4.8-km resolution) and the Applications of Research to Operations at Mesoscale (AROME; 2.5-km resolution). Based on a case study and a longer-term validation, it is shown that, generally, the finer the grid spacing of the background model and the higher the resolution of the target grid in the downscaling procedure, the slightly more accurate is the analysis. This is especially true for wind analyses in mountainous regions, where a realistic simulation of topographic effects is crucial. In the case of 2-m temperature, the impact is less pronounced, but the topographic downscaling at very high resolution at least adds detail in complex terrain. However, in the vicinity of station observations, the analysis algorithm is capable of spatially adjusting the larger biases found in the ALARO model while having a lesser effect on the downscaled AROME model. [ABSTRACT FROM AUTHOR]

The National Oceanic and Atmospheric Administration/Earth System Research Laboratory (NOAA/ESRL) generated a multidecadal (from 1985 to present) ensemble reforecast database for the 2012 version of the Global Ensemble Forecast System (GEFS). This dataset includes 11-member reforecasts initialized once per day at 0000 UTC. This GEFS version has a strong cold bias for winter and warm bias for summer in the Northern Hemisphere. Although the operational decaying average bias-correction approach performs well in winter and summer, it sometimes fails during the spring and fall transition seasons at long lead times (>~5 days). In this paper, 24- (1985-2008) and 25-yr (1985-2009) reforecast biases are used to calibrate 2-m temperature forecasts in 2009 and 2010, respectively. The reforecast-calibrated forecasts for both years are more accurate than those adjusted by the decaying average method during transition seasons. A long training period (>5 yr) is necessary to help avoid a large impact on bias correction from an extreme year case and keep a broader diversity of weather scenarios. The improvement from using the full 25-yr, 31-day window, weekly training dataset is almost equivalent to that from using daily training samples. This provides an option to reduce computational expenses while maintaining a desired accuracy. To provide the potential to improve forecast accuracy for transition seasons, reforecast information is added into the current operational bias-correction method. The relative contribution of the two methods is determined by the correlation between the ensemble mean and analysis. This method improves the forecast accuracy for most of the year with a maximum benefit during April-June. [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]

This paper evaluates, for the first time, flash-flood guidance (FFG) values and recently developed gridded FFG (GFFG) used by the National Weather Service (NWS) to monitor and predict imminent flash flooding, which is the leading storm-related cause of death in the United States. It is envisioned that results from this study will be used 1) to establish benchmark performance of existing operational flash-flood prediction tools and 2) to provide information to NWS forecasters that reveals how the existing tools can be readily optimized. Sources used to evaluate the products include official reports of flash floods from the NWS Storm Data database, discharge measurements on small basins available from the U.S. Geological Survey, and witness reports of flash flooding collected during the Severe Hazards Analysis and Verification Experiment. Results indicated that the operational guidance values, with no calibration, were marginally skillful, with the highest critical success index of 0.20 occurring with 3-h GFFG. The false-alarm rates fell and the skill improved to 0.34 when the rainfall was first spatially averaged within basins and then reached 50%% of FFG for 1-h accumulation and exceeded 3-h FFG. Although the skill of the GFFG values was generally lower than that of their FFG counterparts, GFFG was capable of detecting the spatial variability of reported flash flooding better than FFG was for a case study in an urban setting. [ABSTRACT FROM AUTHOR]

A new scheme is developed to improve the seasonal prediction of summer precipitation in the East Asian and western Pacific region. The scheme is applied to the Development of a European Multimodel Ensemble System for Seasonal to Interannual Prediction (DEMETER) results. The new scheme is designed to consider both model predictions and observed spatial patterns of historical “analog years.” In this paper, the anomaly pattern correlation coefficient (ACC) between the prediction and the observation, as well as the root-mean-square error, is used to measure the prediction skill. For the prediction of summer precipitation in East Asia and the western Pacific (0°–40°N, 80°–130°E), the prediction skill for the six model ensemble hindcasts for the years of 1979–2001 was increased to 0.22 by using the new scheme from 0.12 for the original scheme. All models were initiated in May and were composed of nine member predictions, and all showed improvement when applying the new scheme. The skill levels of the predictions for the six models increased from 0.08, 0.08, 0.01, 0.14, -0.07, and 0.07 for the original scheme to 0.11, 0.14, 0.10, 0.22, 0.04, and 0.13, respectively, for the new scheme. [ABSTRACT FROM AUTHOR]

Rank histograms are a commonly used tool for evaluating an ensemble forecasting system’s performance. Because the sample size is finite, the rank histogram is subject to statistical fluctuations, so a goodness-of-fit (GOF) test is employed to determine if the rank histogram is uniform to within some statistical certainty. Most often, the χ2 test is used to test whether the rank histogram is indistinguishable from a discrete uniform distribution. However, the χ2 test is insensitive to order and so suffers from troubling deficiencies that may render it unsuitable for rank histogram evaluation. As shown by examples in this paper, more powerful tests, suitable for small sample sizes, and very sensitive to the particular deficiencies that appear in rank histograms are available from the order-dependent Cramér–von Mises family of statistics, in particular, the Watson and Anderson–Darling statistics. [ABSTRACT FROM AUTHOR]

In an ensemble Kalman filter, when the analysis update of an ensemble member is computed using error statistics estimated from an ensemble that includes the background of the member being updated, the spread of the resulting ensemble systematically underestimates the uncertainty of the ensemble mean analysis. This problem can largely be avoided by applying cross validation: using an independent subset of ensemble members for updating each member. However, in some circumstances cross validation can lead to the divergence of one or more ensemble members from observations. This can culminate in catastrophic filter divergence in which the analyzed or forecast states become unrealistic in the diverging members. So far, such instabilities have been reported only in the context of highly nonlinear low-dimensional models. The first known manifestation of catastrophic filter divergence caused by the use of cross validation in an NWP context is reported here. To reduce the risk of such filter divergence, a modification to the traditional cross-validation approach is proposed. Instead of always assigning the ensemble members to the same subensembles, the members forming each subensemble are randomly chosen at every analysis step. It is shown that this new approach can prevent filter divergence and also brings a cycling ensemble data assimilation system containing divergent members back to a state consistent with Gaussianity. The randomized subensemble approach was implemented in the operational global ensemble prediction system at Environment and Climate Change Canada on 1 December 2021. [ABSTRACT FROM AUTHOR]

A forecast "bust" or "dropout" can be defined as an intermittent but significant loss of model forecast performance. Deterministic forecast dropouts are typically defined in terms of the 500-hPa geopotential height (Φ500) anomaly correlation coefficient (ACC) in the Northern Hemisphere (NH) dropping below a predefined threshold. This study first presents a multimodel comparison of dropouts in the Navy Global Environmental Model (NAVGEM) deterministic forecast with the ensemble control members from the Environment and Climate Change Canada (ECCC) Global Ensemble Prediction System (GEPS) and the National Centers for Environmental Prediction (NCEP) Global Ensemble Forecast System (GEFS). Then, the relationship between dropouts and large-scale pattern variability is investigated, focusing on the temporal variability and correlation of flow indices surrounding dropout events. Finally, three severe dropout events are examined from an ensemble perspective. The main findings of this work are the following: 1) forecast dropouts exhibit some relation between models; 2) although forecast dropouts do not have a single cause, the most severe dropouts in NAVGEM can be linked to specific behavior of the large-scale flow indices, that is, they tend to follow periods of rapidly escalating volatility of the flow indices, and they tend to occur during intervals where the AO and Pacific North American (PNA) indices are exhibiting unusually strong interdependence; and 3) for the dropout events examined from an ensemble perspective, the NAVGEM ensemble spread does not provide a strong signal of elevated potential for very large forecast errors. [ABSTRACT FROM AUTHOR]

The approach of applying different amounts of horizontal localization to different ranges of background-error covariance horizontal scales as proposed by Buehner and Shlyaeva was recently implemented in the four-dimensional ensemble–variational (4DEnVar) data assimilation scheme of the global deterministic prediction system (GDPS) at Environment and Climate Change Canada operations. To maximize the benefits from this approach to reduce the sampling noise in the ensemble-derived background-error covariances, it was necessary to adopt a new weighting between the climatological and flow-dependent covariances that increases significantly the role of the latter. Thus, in December 2021 the GDPS became the first operational global deterministic medium-range weather forecasting system to rely completely on flow-dependent covariances in the troposphere and the lower stratosphere. The experiments that led to the adoption of these two related changes and their impacts on the forecasts up to 7 days for various regions of the globe during the boreal summer of 2019 and winter of 2020 are presented here. It is also illustrated that relying more on ensemble-derived covariances amplifies the positive impacts on the GDPS when the background ensemble generation strategy is improved. [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]

A new ensemble-based land surface data assimilation (DA) system is coupled with the atmospheric four-dimensional ensemble-variational data assimilation (4D-EnVar) system with the goal of improving the analyses within Environment and Climate Change Canada's Global Deterministic Prediction System. Since 2001, the sequential assimilation of surface variables is used to generate the initial conditions to launch the Global Environmental Multiscale (GEM) coupled forecast model. The work presented here is to replace the sequential DA with an independent surface DA system, the Canadian Land Data Assimilation System (CaLDAS) assimilating screen-level observations, and to compare assimilation experiments with CaLDAS run in uncoupled and weakly coupled modes. In the uncoupled mode, CaLDAS is used to initialize the forecast without interacting with the 4D-EnVar system. In the coupled mode, the analyses generated from CaLDAS and 4D-EnVar are used to initialize the forecast model. The analyses and forecasts from uncoupled and coupled runs are evaluated against surface and radiosonde observations over different subdomains to conclude the impact of coupling CaLDAS with 4D-EnVar. Results indicate a statistically significant reduction in bias and standard deviation at the surface for screen-level temperature and dewpoint temperature on the order of 0.1 K, and in the lower troposphere between 1000 and 500 hPa on the order of 0.1 dam for geopotential height and 0.1 K for air temperature and dewpoint depression in the coupled DA runs. The positive impact persists up to 5 days over some subdomains. It is concluded that the coupled DA approach generally performs better than the uncoupled version. [ABSTRACT FROM AUTHOR]

The Center for Weather Forecast and Climate Studies [Centro de Previsão e Tempo e Estudos Climáticos (CPTEC)] at the Brazilian National Institute for Space Research [Instituto Nacional de Pesquisas Espaciais (INPE)] has recently operationally implemented a three-dimensional variational data assimilation (3DVAR) scheme based on the Gridpoint Statistical Interpolation analysis system (GSI). Implementation of the GSI system within the atmospheric global circulation model from CPTEC/INPE (AGCM-CPTEC/INPE) is hereafter referred to as the Global 3DVAR (G3DVAR) system. The results of an observing system experiment (OSE) measuring the impacts of radiosonde, satellite radiance, and GPS radio occultation (RO) data on the new G3DVAR system are presented here. The observational impact of each of these platforms was evaluated by measuring the degradation of the geopotential height anomaly correlation and the amplification of the RMSE of the wind. Losing the radiosonde, GPS RO, and satellite radiance data in the OSE resulted in negative impacts on the geopotential height anomaly correlations globally. Nevertheless, the strongest impacts were found over the Southern Hemisphere and South America when satellite radiance data were withheld from the data assimilation system. [ABSTRACT FROM AUTHOR]

Winter storms in the southern United States can significantly impact infrastructure and the economy. In this study, National Centers for Environmental Information Storm Event Database and local climate summaries, are used to develop a spatial climatology of freezing precipitation (freezing rain and ice pellets) and snow over the southern Great Plains, 1993-2011. Principal component analysis is performed on the 500-hPa height field, at the approximate onset time of precipitation, for 33 freezing precipitation and 42 snow case studies, to differentiate common synoptic flow fields associated with precipitation type. The five leading patterns for each precipitation type are retained. Composites of temperature, moisture, pressure, and wind fields are constructed and extended 24 h before and after precipitation initiation to track the storm system evolution. Many 500-hPa flow fields are similar for both precipitation types. However, snow-dominant events have stronger and/or more frequent surface cyclone development. Freezing precipitation is associated with the southward propagation of an Arctic anticyclone well ahead of precipitation, weak or absent surface cyclone formation, and a more western trough axis. High-impact ice storms in the region often have slow-moving upper-level flow, persistent isentropic ascent over a surface quasi-stationary front with strongly positive moisture anomalies, and warm layer airmass trajectories originating over the Gulf of Mexico. The results here are based on a relatively small sample size. However, this work is intended to be useful for forecasters, in particular as a pattern recognition aid in predicting the evolution of precipitation within southern Great Plains winter storms. [ABSTRACT FROM AUTHOR]

The probabilistic prediction of tropical cyclone (TC) rapid intensification (RI) in the Atlantic and eastern Pacific Ocean basins is examined here using a series of logistic regression models trained on environmental and infrared satellite-derived features. The environmental predictors are based on averaged values over a 24-h period following the forecast time. These models are compared against equivalent models enhanced with additional TC predictors created from passive satellite microwave imagery (MI). Leave-one-year-out cross validation on the developmental dataset shows that the inclusion of MI-based predictors yields more skillful RI models for a variety of RI and intensity thresholds. Compared with the baseline forecast skill of the non-MI-based RI models, the relative skill improvements from including MI-based predictors range from 10.6% to 44.9%. Using archived real-time data during the period 2004-13, evaluation of simulated real-time models is also carried out. Unlike in the model development stage, the simulated real-time setting involves using Global Forecast System forecasts for the non-satellite-based predictors instead of 'perfect' observational-based predictors in the developmental data. In this case, the MI-based RI models still generate superior skill to the baseline RI models lacking MI-based predictors. The relative improvements gained in adding MI-based predictors are most notable in the Atlantic, where the non-MI versions of the models suffer acutely from the use of imperfect real-time data. In the Atlantic, relative skill improvements provided from the inclusion of MI-based predictors range from 53.5% to 103.0%. The eastern Pacific relative improvements are less impressive but are still uniformly positive. [ABSTRACT FROM AUTHOR]

The climate of Saudi Arabia is arid-semiarid with infrequent but sometimes intense rainfall, which can cause flooding. Interannual and intraseasonal precipitation variability in the region is related to ENSO and MJO tropical convection. The predictability of these tropical signals gives some expectation of skillful extended-range rainfall forecasts in the region. Here, the extent to which this predictability is realizable in the Climate Forecast System (CFS), version 2, a state-of-the-art coupled global ocean-atmosphere model, is assessed. While there are deficiencies in the forecast climatology likely related to orography and resolution, as well as lead-dependent biases, CFS represents the climatology of the region reasonably well. Forecasts of the areal average of rainfall over Saudi Arabia show that the CFS captures some features of a spring 2013 heavy rainfall event up to 10 days in advance and a transition from dry to wet conditions up to 20 days in advance. Analysis of a 12-yr (1999-2010) reforecast dataset shows that the CFS can skillfully predict the rainfall amount, the number of days exceeding a threshold, and the probability of heavy rainfall occurrence for forecast windows ranging from 1 to 30 days. While the probability forecasts show good discrimination, they are overconfident. Logistic regression based on the ensemble mean value improves forecast skill and reliability. Forecast probabilities have a clear relation with the MJO phase in the wet season, providing a physical basis for the observed forecast skill. [ABSTRACT FROM AUTHOR]

An object-based verification technique that keys off the radar-retrieved vertically integrated liquid (VIL) is used to evaluate how well the High-Resolution Rapid Refresh (HRRR) predicted mesoscale convective systems (MCSs) in 2012 and 2013. It is found that the modeled radar VIL values are roughly 50% lower than observed. This mean bias is accounted for by reducing the radar VIL threshold used to identify MCSs in the HRRR. This allows for a more fair evaluation of the model's skill at predicting MCSs. Using an optimized VIL threshold for each summer, it is found that the HRRR reproduces the first (i.e., counts) and second moments (i.e., size distribution) of the observed MCS size distribution averaged over the eastern United States, as well as their aspect ratio, orientation, and diurnal variations. Despite threshold optimization, the HRRR tended to predict too many (few) MCSs at lead times less (greater) than 4 h because of lead time-dependent biases in the modeled radar VIL. The HRRR predicted too many MCSs over the Great Plains and too few MCSs over the southeastern United States during the day. These biases are related to the model's tendency to initiate too many MCSs over the Great Plains and too few MCSs over the southeastern United States. Additional low biases found over the Mississippi River valley region at night revealed a tendency for the HRRR to dissipate MCSs too quickly. The skill of the HRRR at predicting specific MCS events increased between 2012 and 2013, coinciding with changes in both the model physics and in the methods used to assimilate the three-dimensional radar reflectivity. [ABSTRACT FROM AUTHOR]

On 31 May 2013, a supercell produced a tornado rated as 3 on the enhanced Fujita scale (EF3) near El Reno, Oklahoma, which was sampled by the S-band phased-array radar (PAR) at the National Weather Radar Testbed in Norman, Oklahoma. Collaboration with the forecaster who issued tornado warnings for the El Reno supercell during real-time operations focused the analysis on critical radar signatures frequently assessed during warning operations. The wealth of real-world experience provided by the forecaster, along with the quantitative analysis, highlighted differences between rapid-scan PAR data and the Weather Surveillance Radar-1988 Doppler located near Oklahoma City, Oklahoma (KTLX), within the context of forecast challenges faced on 31 May 2013. The comparison revealed that the 70-s PAR data proved most advantageous to the forecaster's situational awareness in instances of rapid storm organization, sudden mesocyclone intensification, and abrupt, short-term changes in tornado motion. Situations where PAR data were most advantageous in the depiction of storm-scale processes included 1) rapid variations in mesocyclone intensity and associated changes in inflow magnitude; 2) imminent radar-indicated development of the short-lived (EF0) Calumet, Oklahoma, and long-lived (EF3) El Reno tornadoes; and 3) precise location and motion of the tornado circulation. As a result, it is surmised that rapid-scan volumetric radar data in cases like this would augment a forecaster's ability to observe rapidly evolving storm features and deliver timely, life-saving information to the general public. [ABSTRACT FROM AUTHOR]

The occurrence of diurnal afternoon convection in Taiwan undergoes substantial modulation from tropical intraseasonal oscillations in the western North Pacific, including the quasi-biweekly (QBW) mode. By analyzing surface station observations and the Climate Forecast System (CFS) Reanalyses (CFSR), as well as the NCEP CFS version 2 (CFSv2) reforecast data over 18 summers from 1993 to 2010, it was found that the QBW mode plays a significant role in the formation of episodic diurnal convection. When the cyclonic circulation of the QBW mode is located west of Taiwan, followed by an anticyclonic circulation to the east, Taiwan's diurnal convection activity tends to intensify and persists for about 4-7 days. Synoptically, this situation reflects the enhanced subtropical anticyclone leading to fair weather conditions and increased monsoon southwesterly winds moistening the lower troposphere, all of which are conducive to thermally induced diurnal convection in Taiwan. The opposite situation tends to suppress the diurnal convection activity for a sustained period of time. Based upon this synoptic linkage, an empirical relationship between the precipitation diurnal amplitude and low-level circulation fields of the CFSv2 is derived. It was found that the CFSv2 forecast exhibits an effective lead time ranging from 16 to 24 days for the QBW mode and, subsequently, diurnal convection episodes in Taiwan. [ABSTRACT FROM AUTHOR]

The character of fog at Cape Town International Airport (CTIA), South Africa, is investigated, using 13 yr of historical hourly data during the period 1997-2010. Hourly surface observations are used to identify fog types that most frequently affect CTIA, by using an objective fog-type classification method that classifies fog events according to their primary formation mechanisms. Fog-type characteristics, such as the minimum visibility, duration, and time of onset and dissipation, are determined. Self-organizing maps (SOMs) are used to determine the dominant synoptic circulation types associated with fog at CTIA. Results show that radiative processes are the most common cause of fog, with an enhanced likelihood of radiation fog in winter. Cloud-base-lowering fog and advection fog events are more likely at the start of the fog season. As the fog season (March-August) progresses, synoptic circulations associated with fog change from a dominant low pressure pattern along the west coast in March and April to a dominant interior high pressure pattern toward July and August. The techniques presented may be used to provide aviation forecasters with a detailed description of the types of fog that frequently occur, their characteristics, and the synoptic circulation associated therewith. [ABSTRACT FROM AUTHOR]

Three real-time storm surge forecasting systems [the eight-member Stony Brook ensemble (SBSS), the Stevens Institute of Technology's New York Harbor Observing and Prediction System (SIT-NYHOPS), and the NOAA Extratropical Storm Surge (NOAA-ET) model] are verified for 74 available days during the 2007-08 and 2008-09 cool seasons for five stations around the New York City-Long Island region. For the raw storm surge forecasts, the SIT-NYHOPS model has the lowest root-mean-square errors (RMSEs) on average, while the NOAA-ET has the largest RMSEs after hour 24 as a result of a relatively large negative surge bias. The SIT-NYHOPS and SBSS also have a slight negative surge bias after hour 24. Many of the underpredicted surges in the SBSS ensemble are associated with large waves at an offshore buoy, thus illustrating the potential importance of nearshore wave breaking (radiation stresses) on the surge predictions. A bias correction using the last 5 days of predictions (BC) removes most of the surge bias in the NOAA-ET model, with the NOAA-ET-BC having a similar level of accuracy as the SIT-NYHOPS-BC for positive surges. A multimodel surge ensemble (ENS-3) comprising the SBSS control member, SIT-NYHOPS, and NOAA-ET models has a better degree of deterministic accuracy than any individual member. Probabilistically, the ALL ensemble (eight SBSS members, SIT-NYHOPS, and NOAA-ET) is underdispersed and does not improve after applying a bias correction. The ENS-3 improves the Brier skill score (BSS) relative to the best deterministic member (SIT-NYHOPS), and the ENS-3 has a larger BSS and better reliability than the SBSS and ALL ensembles, thus illustrating the benefits of a multimodel storm surge ensemble. [ABSTRACT FROM AUTHOR]

The influence of the Pacific decadal oscillation (PDO) on important hydroclimatic variables during years of neutral ENSO for 84 climate divisions in the western United States is analyzed from 1925 to 1998. When the 34 neutral ENSO years are split by cold (12 yr) and warm (22 yr) PDOs, the resulting winter precipitation patterns are spatially similar to those that occur during years of La Niña–cold PDO and, to a lesser extent, years of El Niño–warm PDO, respectively, although the characteristic ENSO dipole is not evident. The PDO influence is similar when the winter Palmer drought severity index is analyzed, although the core area of influence moves from the Southwest to the northern Rockies. Correlations between Niño-3.4 SSTs and the hydroclimatic variables reverse sign when the neutral ENSO years are split by PDO phase. The greatest difference between correlations occurs in the characteristic dipole between the Pacific Northwest and the desert Southwest. Since seasonal forecast guidance based on ENSO conditions in the tropical Pacific often yields a forecast of “equal chances” during years of neutral ENSO, forecasters may be able to improve their forecasts for the southwestern United States depending on if the PDO is known to be in the cold (drier than normal) or warm (wetter than normal) phase. However, this can be difficult to implement considering the current uncertainty of the phase of the PDO. [ABSTRACT FROM AUTHOR]

Characteristics of the life cycle and motion of convective systems that occurred over the Baja California Peninsula were determined from a case study in September of 2003. This note applies data from satellite imagery, regular observations from upper-air and surface networks, and operational analyses. Changes in environmental conditions over northwestern Mexico are documented and these are associated with the development of Tropical Cyclone Linda in the eastern Pacific Ocean. When Linda became a tropical storm and was located several hundred kilometers away to the southwest, a convective outbreak occurred over land. An examination of large-scale conditions indicated that flow from the eastern flank of Linda supplied low- to midlevel moisture. Significant convection is associated with specific thresholds for precipitable water, CAPE, and lifted index. Convective systems initiated in the early afternoon remained active for several hours and provided localized areas of precipitation along the western side of the peninsular mountains. An assessment of all the available surface data was performed to determine regional elements that played a role in the development of these systems. Results include documentation of a sea breeze from the Gulf of California onto the mountain slopes when organized convection was first detected. [ABSTRACT FROM AUTHOR]

A 30-yr climatology of the snow-to-liquid-equivalent ratio (SLR) using the National Weather Service (NWS) Cooperative Summary of the Day (COOP) data is presented. Descriptive statistics are presented for 96 NWS county warning areas (CWAs), along with a discussion of selected histograms of interest. The results of the climatology indicate that a mean SLR value of 13 appears more appropriate for much of the country rather than the often-assumed value of 10, although considerable spatial variation in the mean exists. The distribution for the entire dataset exhibits positive skewness. Histograms for individual CWAs are both positively and negatively skewed, depending upon the variability of the in-cloud, subcloud, and ground conditions. [ABSTRACT FROM AUTHOR]

GOES-8 visible and infrared cloud frequency composites have been created from imagery collected during June, July, and August for the years 1996–99 over northern Florida. These cloud frequency composites are unique because they offer high-resolution coverage over a small area and have been tailored to address forecast needs. Both monthly and regime cloud frequency composites are presented. Nine regimes were designated to reflect the strength and development of the sea-breeze front under various synoptic winds and the resulting effect on convective development. The regimes were designated by mean boundary layer wind speed and direction over the region of interest. Results from four of the regimes are presented. A total of 222 days (60% of all possible days) were designated for the various wind regimes. Regime 4 (W to SW flow) occurred most frequently (24%) and had the most widespread distribution of higher cloud frequency, occurring both near the coast and inland. Regime 2, with contrasting E to NE flow, was the next most frequently occurring regime (17%) and had lower cloud frequencies, particularly inland in Alabama and Georgia. Regime 5, with strong W to SW flow (15%, not presented) was third, followed by Regime 8 with N to NW flow (13%) and Regime 1 (11%) with light and variable or light SE flow. The monthly composites included the days from the various regime days as well as those with a completely disturbed or completely suppressed sea-breeze circulation. Nonetheless, the influence of the sea-breeze circulation can readily be seen in the diurnal progression of cloud frequency over a month. The variations seen in monthly cloud frequency composites for June, July, and August 1996–99 highlight periods of high and low cloud frequency and offer a different perspective on year-to-year and month-to-month variability. The regime cloud frequency results are actively being used during the summer season in aviation and public forecasting to supplem... [ABSTRACT FROM AUTHOR]