ALBERT

Description: Valley cold pools (VCPs), which are trapped, cold layers of air at the bottoms of basins or valleys, pose a significant problem for forecasters because they can lead to several forms of difficult-to-forecast and hazardous weather such as fog, freezing rain, or poor air quality. Numerical models have historically failed to routinely provide accurate guidance on the formation and demise of VCPs, making the forecast problem more challenging. In some case studies of persistent wintertime VCPs, there is a connection between the movement of upper-level waves and the timing of VCP formation and decay. Herein, a 3-yr climatology of persistent wintertime VCPs for five valleys and basins in the western United States is performed to see how often VCP formation and decay coincides with synoptic-scale (∼200–2000 km) wave motions. Valley cold pools are found to form most frequently as an upper-level ridge approaches the western United States and in response to strong midlevel warming. The VCPs usually last as long as the ridge is over the area and usually only end when a trough, and its associated midlevel cooling, move over the western United States. In fact, VCP strength appears to be almost entirely dictated by midlevel temperature changes, which suggests large-scale forcing is dominant for this type of VCP most of the time.

Description: A specific event, called a low-visibility procedure (LVP), has been defined when visibility is under 600 m and/or the ceiling is under 60 m at Paris-Charles de Gaulle Airport, Paris, France, to ensure air traffic safety and to reduce the economic issues related to poor visibility conditions. The Local Ensemble Prediction System (LEPS) has been designed to estimate LVP likelihood in order to help forecasters in their tasks. This work evaluates the skill of LEPS for each type of LVP that takes place at the airport area during five winter seasons from 2002 to 2007. An event-based classification reveals that stratus base lowering, advection, and radiation fogs make up for 78% of the LVP cases that occurred near the airport during this period. This study also demonstrates that LEPS is skillful on these types of event for short-term forecasts. When the ensemble runs start with initialized LVP events, the prediction of advection fogs is as skillful as the prediction of radiation fog events and stratus base lowering. At 3 and 6 h before the runs where LVP events were initialized, LEPS still shows positive skill for radiation fog events and stratus base lowering cases.

Description: A technique to identify and quantify intense convection in tropical cyclones (TCs) using bispectral, geostationary satellite imagery is explored. This technique involves differencing the water vapor (WV) and infrared window (IRW) channel brightness temperature values, which are available on all current operational geostationary weather satellites. Both the derived IRW minus WV (IRWV) imagery and the raw data values can be used in a variety of methods to provide TC forecasters with important information about current and future intensity trends, a component within the operational TC forecasting arena that has shown little improvement during the past few decades. In this paper several possible uses for this bispectral technique, both qualitative and quantitative, are explored and outlined. Qualitative monitoring of intense convection can be used as a proxy for passive microwave (MW) imager data obtained from polar-orbiting satellite platforms when not available. In addition, the derived imagery may aid in the TC storm center identification process, both manually and objectively, especially in difficult situations where the IRW imagery alone cannot be used such as when the storm circulation center and/or eye features are obscured by a cirrus canopy. Quantitative methods discussed involve the predictive quality of the IRWV data in terms of TC intensity changes, primarily during TC intensification. Strong correlations exist between storm intensity changes and IRWV values at varying 6-h forecast interval periods, peaking between the 12- and 24-h time periods. Implications for the use of the IRWV data on such objective satellite intensity estimate algorithms as the University of Wisconsin—Madison (UW) Cooperative Institute for Meteorological Satellite Studies (CIMSS) advanced Dvorak technique (ADT) are also discussed.

Description: 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.

Description: The National Hurricane Center (NHC) Hurricane Probability Program (HPP) was implemented in 1983 to estimate the probability that the center of a tropical cyclone would pass within 60 n mi of a set of specified points out to 72 h. Other than periodic updates of the probability distributions, the HPP remained unchanged through 2005. Beginning in 2006, the HPP products were replaced by those from a new program that estimates probabilities of winds of at least 34, 50, and 64 kt, and incorporates uncertainties in the track, intensity, and wind structure forecasts. This paper describes the new probability model and a verification of the operational forecasts from the 2006–07 seasons. The new probabilities extend to 120 h for all tropical cyclones in the Atlantic and eastern, central, and western North Pacific to 100°E. Because of the interdependence of the track, intensity, and structure forecasts, a Monte Carlo method is used to generate 1000 realizations by randomly sampling from the operational forecast center track and intensity forecast error distributions from the past 5 yr. The extents of the 34-, 50-, and 64-kt winds for the realizations are obtained from a simple wind radii model and its underlying error distributions. Verification results show that the new probability model is relatively unbiased and skillful as measured by the Brier skill score, where the skill baseline is the deterministic forecast from the operational centers converted to a binary probabilistic forecast. The model probabilities are also well calibrated and have high confidence based on reliability diagrams.

Description: The Sierra Nevada of eastern California receives heavy snowfall each year. However, it is the snowstorms that deposit heavy snowfall in a relatively short period of time that can cause major inconveniences and even life-threatening situations for the residents and visitors to the region. Some of these snowstorms are so extreme as to become legendary, and with increased population in this region a synoptic climatology of these extreme snowstorms can be a useful tool for assessing snowfall potential by operational forecasters. Additionally, the hydrological and climatological implications of extreme Sierra Nevada snowfalls are important for state and local resource- and emergency-planning purposes. A climatology of these snowstorms will be presented. The period of study will include the snowfall seasons (October–May) 1949/50 through 2004/05. A total of 542 snowstorms occurred during these 56 snowfall seasons. These snowstorms were analyzed to determine any common synoptic features. The most intense snowstorms in the highest decile of snowfall totals were analyzed in more detail in order to determine the parameters associated with these strongest snowstorms. Upper-level synoptic and thermodynamic characteristics associated with each snowstorm were then diagnosed to determine what common synoptic hydrodynamic and thermodynamic parameters the snowstorms share. Synoptic patterns were studied using the National Centers for Environmental Prediction (NCEP) model reanalysis data. Wind speeds at 200 hPa, and height anomalies at 500 hPa, were analyzed for each snowstorm from 3 days prior to the start of snowfall and continuing through the end of the storm. Anomalies and the transport of precipitable water were studied in order to determine the relative amount of moisture that was available to each snowstorm. A conceptual model for forecasting the strongest snowstorms was developed. Key findings include the following: 1) the importance of a fetch of moisture from the subtropics with relatively large positive moisture anomalies, 2) the importance of the atmospheric moisture stream being normal to the Sierra, 3) the low static stability accompanying these snowstorms, and 4) the importance of relatively strong upper-level dynamics, which helped to intensify the systems as they approached the Sierra.

Description: The National Centers for Environmental Prediction’s (NCEP) Eta Model, the models of the European Centre for Medium-Range Weather Forecasts (ECMWF) and the National Aeronautics and Space Administration’s (NASA) Global Modeling and Assimilation Office (GMAO) models, and the Regional Atmospheric Modeling System (RAMS) model are all examined during the Mixed-Phase Arctic Clouds Experiment (MPACE) that took place from 27 September through 22 October 2004. During two intensive observation periods, soundings were launched every 6 h from four sites across the North Slope of Alaska (NSA): Barrow, Atqasuk, Oliktok Point, and Toolik Lake. Measurements of temperature, moisture, and winds, along with surface measurements of radiation and cloud cover, were compared to model outputs from the Eta, ECMWF, GMAO, and RAMS models using the bootstrap statistical technique to ascertain if differences in model performance were statistically significant. Ultimately, three synoptic regimes controlled NSA weather during the MPACE period for varying amounts of time. Each posed a unique challenge to the forecasting models during the study period. Temperature forecasts for all models were good at the MPACE sites with mean bias errors generally under 2 K, and the models had the fewest significant errors predicting temperature. Forecasting moisture and wind proved to be more difficult for the models, especially aloft in the 500–300-hPa layer. The largest errors occurred in the GMAO model, with significant moist biases of 40% and wind errors of 10 m s−1 or more. The RAMS, Eta, and ECMWF models had smaller moist biases in this layer. Both the Eta and RAMS models overestimated the surface incident shortwave radiation, underestimated longwave radiation, and underestimated cloud cover fraction. Overall, the bootstrapping results coincided with findings from conventional statistical comparisons as model outputs with the largest errors were most likely to be captured and declared statistically significant in the bootstrapping process. The significant model errors during MPACE were predominantly traced to the inability of the models to simulate disturbances in synoptic regime I, warm or cold biases over higher inland terrain, a warm bias along the NSA coastal waters in the Beaufort Sea, and difficulty in forecasting the intensity of the explosive cyclone in synoptic regime III.

Description: 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.

Description: Assimilation experiments have been performed with the Weather Research and Forecasting (WRF) model’s three-dimensional variational data assimilation (3DVAR) scheme to assess the impacts of NASA’s Quick Scatterometer (QuikSCAT) near-surface winds, and Special Sensor Microwave Imager (SSM/I) wind speed and total precipitable water (TPW) on the analysis and on short-range forecasts over the Indian region. The control (without satellite data) as well as WRF 3DVAR sensitivity runs (which assimilated satellite data) were made for 48 h starting daily at 0000 UTC during July 2006. The impacts of assimilating the different satellite dataset were measured in comparison to the control run, which does not assimilate any satellite data. The spatial distribution of the forecast impacts (FIs) for wind, temperature, and humidity from 1-month assimilation experiments for July 2006 demonstrated that on an average, for 24- and 48-h forecasts, the satellite data provided useful information. Among the experiments, WRF wind speed prediction was improved by QuikSCAT surface wind and SSM/I TPW assimilation, while temperature and humidity prediction was improved due to the assimilation of SSM/I TPW. The rainfall prediction has also been improved significantly due to the assimilation of SSM/I TPW, with the largest improvement seen over the west coast of India. Through an improvement of the surface wind field, the QuikSCAT data also yielded a positive impact on the precipitation, particularly for day 1 forecasts. In contrast, the assimilation of SSM/I wind speed degraded the humidity and rainfall predictions.

Description: Three spatial verification techniques are applied to three datasets. The datasets consist of a mixture of real and artificial forecasts, and corresponding observations, designed to aid in better understanding the effects of global (i.e., across the entire field) displacement and intensity errors. The three verification techniques, each based on well-known statistical methods, have little in common and, so, present different facets of forecast quality. It is shown that a verification method based on cluster analysis can identify “objects” in a forecast and an observation field, thereby allowing for object-oriented verification in the sense that it considers displacement, missed forecasts, and false alarms. A second method compares the observed and forecast fields, not in terms of the objects within them, but in terms of the covariance structure of the fields, as summarized by their variogram. The last method addresses the agreement between the two fields by inferring the function that maps one to the other. The map—generally called optical flow—provides a (visual) summary of the “difference” between the two fields. A further summary measure of that map is found to yield useful information on the distortion error in the forecasts.