United States DEPARTMENT of Commerce

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A P P E N D I x C : P e r f o r m a n c e M e a s u r e s D e f i n i t i o n s Performance Measure: Accuracy (%) (threat score) of day 1 precipitation forecasts This performance measure tracks the ability of the weather forecasters of NOAA’s Hydrometeorological Prediction Center (HPC) to predict accurately the occurrence of one inch or more of precipitation (rain or the water equivalent of melted snow or ice pellets) 24 hours in advance across the contiguous United States. Through this measure, HPC focuses on relatively heavy amounts of precipitation, usually a half inch or more in a 24-hour period (short-term flood and flash flood warnings), because of the major safety and economic impacts such heavy precipitation can have in producing flooding, alleviating drought, and affecting river navigation. These forecasts indicate how much precipitation is expected across the United States, not just whether it will rain or snow. HPC tracks the accuracy of these forecasts using a metric with the statistical name of “threat score” or equivalently “critical success indicator.” This accuracy metric ranges from zero percent, indicating no skill, to 100 percent for a perfect forecast. For example, in verifying the accuracy of a forecast of one inch or more of precipitation for day 1 (the next 24 hours), HPC first determines everywhere in the United States where an inch or more actually fell and was observed by rain gauges. On a given day this occurs only over a very small percentage of the country (although a one inch or more precipitation event is significant for the inhabitants of that particular area). HPC then compares these observed areas of at least one inch of precipitation with the forecasted areas of at least one inch, counting only those points in the United States where HPC forecasted and observed at least an inch as being an accurate forecast. (These points are called “hits.”) Thus, if HPC forecasts one inch to fall at the point representing Washington, DC, and it observed only three-quarters of an inch actually had fallen in that specific area, the forecast is then rated as a miss, even if an inch of rain was observed to have fallen at the points nearby The overall accuracy score for the country for that particular day 1 forecast is then determined by dividing the total number of correctly forecast points (hits) by the total number of points where HPC had either forecast at least one inch of liquid precipitation or one inch of liquid precipitation had actually occurred. Thus this measure takes into consideration those areas where one inch or more of precipitation was correctly forecast, where it was forecasted but did not occur, and where it occurred but had not been forecasted. To earn a high accuracy score, HPC has to forecast the time, place, and amount of precipitation very well. Data Source Frequency Data Storage Internal Controls Data Limitations Actions to be Taken HPC and state agencies Monthly World Weather Building HPC has produced Quantitative Precipitation Forecasts since the early 1960s and has kept verification statistics related to the Quantitative Precipitation Forecast program since that time. HPC forecasters work under the supervisory control of the Senior Branch Forecaster (SBF), who is responsible for the quality and content of all products issued during the shift. The SBF having the additional duty of 24-hour precipitation forecast verification verifies the precipitation forecasts. All data are examined for accuracy and quality control procedures are applied, as described in the Description of Measure section. Verification is the process of comparing the predicted precipitation amounts to the observed amounts over the conterminous United States. All data are reported on to NWS and NOAA leadership on a monthly basis. The 40-year record of performance indicates there can be considerable variation in the performance measure from year to year. This variation is heavily dependent on the variation of weather regimes over the course of a year and from year to year. Scores are usually lower, for example, in years with considerable summertime precipitation not associated with tropical cyclones. NOAA will implement planned weather observation and numerical modeling improvements along with ongoing research projects. The Hydrometeorological Test Bed will be expanded to accelerate the transition of research advancements into the operational prediction of precipitation. 388 F Y 2 0 0 7 P E R F O R M A N C E A N D A C C O U N T A B I L I T Y R E P O R T
A P P E N D I x C : P e r f o r m a n c e M e a s u r e s D e f i n i t i o n s Performance Measures: Winter storm warnings – Lead time (hours) Winter storm warnings – Accuracy (%) A winter storm warning provides NOAA customers and partners advanced notice of a hazardous winter weather event that endangers life or property, or provides an impediment to commerce. Winter storm warnings are issued for winter weather phenomena like blizzards, ice storms, heavy sleet, and heavy snow. These measures reflect advance warning lead time and the accuracy of winter storm events. Improving the accuracy and advance warnings of winter storms enables the public to take the necessary steps to prepare for disruptive winter weather conditions. Data Source Frequency Data Storage Internal Controls Data Limitations Actions to be Taken NWS field offices Monthly The regional headquarters, NWS headquarters, and OCWWS Verification is the process of comparing predicted weather to a reported event. Warnings are collected from each NWS office, quality controlled, and matched to confirmed winter storm reports. Reports are validated by WFOs using concise and stringent guidelines outlined in NWS Instruction 10-1605. OCWWS monitors monthly performance throughout NWS, and the regional headquarters monitor performance within their respective regions. All data are reported on to NWS and NOAA leadership on a quarterly basis. While long-term performance has shown steady increase in forecast accuracy, interannual scores tend to fluctuate due to varying weather patterns from year to year. Some weather patterns are more difficult to forecast than others. Review all warnings and storm data after each event to learn from past experiences. Use the information learned to improve forecast skill and product quality in the future. Performance Measure: Cumulative percentage of U.S. shoreline and inland areas that have improved ability to reduce coastal hazard impacts This measure tracks improvements in NOAA’s ability to assist coastal areas by estimating the risks of natural hazards. Activities are underway to develop a coastal risk atlas that will enable communities to evaluate the risk, extent, and severity of natural hazards in coastal areas. The risk atlas will help coastal communities make more effective hazard mitigation decisions to reduce impacts to life and property. Through the coastal risk atlas, National Ocean Service (NOS) provides a mechanism for coastal communities to evaluate their risks and vulnerabilities to natural hazards and improve their hazard mitigation planning capabilities. Data Source NOS Coastal Services Center; National Satellite, Data, and Information Service (NESDIS); National Coastal Data Development Center; and other federal and state agencies Frequency Data Storage Internal Controls Data Limitations Actions to be Taken Annually NOS and NESDIS will collect information, conduct assessments, and store data. This measure tracks the cumulative percent of shoreline and inland areas with improved ability to reduce the impact of coastal hazards. In the past, the types of projects included in the reported results differed from one year to the next; therefore, the potential for counting a portion of the shoreline more than once existed. For example, one year a project may improve an area’s ability to reduce the impacts of hurricanes, and then another year a separate project may improve the same area’s ability to reduce the impacts of another coastal hazard, such as inland flooding. To avoid confusion, this measure currently only tracks the development and implementation of the Coastal Risk Atlas. All data used in the Coastal Risk Atlas are quality controlled and the risk assessment methodologies have been peer reviewed with quarterly reporting on performance to NOAA Deputy Under Secretary. This measure tracks the development and implementation of the Coastal Risk Atlas as an indicator of improved ability to identify the extent and severity of coastal hazards. Reaching these targets will depend on the activities of other federal and state agencies with management responsibilities in this area. None F Y 2 0 0 7 P E R F O R M A N C E A N D A C C O U N T A B I L I T Y R E P O R T 389
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United States DEPARTMENT of Commerc
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U.S. Department of Commerce FY 2007
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A P P E N D I X A : P E R F O R M A
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M a n ag e m e n t D i s c u s s i
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Number of Targets Number of Targets
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1,653 892 A P P E N D I X A : P E R
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P e r f o r m a n c e S e c t i o n
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S t r at e g i c G oa l 1
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s t r a t e g i c g o a l 1 * P e r
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M Aa Nn Aa Gg Ee Mm Ee Nn T t I Nn
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M A N A G E M E N T I N T E G R A T
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F i n a n c i a l S e c t i o n
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F i n a n c i a l M a n ag e m e n
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F i n a n c i a l M a n a g e m e n
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P R I N C I P A L F I N A N C I A L
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P R I N C I P A L F I N A N C I A L
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Notes to the Financial Statements (
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N O T E S T O T H E F I N A N C I A
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Consolidating Balance Sheet
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C O N S O L I D A T I N G B A L A N
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R e q u i r e d s u p p l e m e n t
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R e q u i r e d s u p p l e m e n t
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R e q u i r e d S u p p l e m e n t
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I n d e p e n d e n t A u d i t o r
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United States DEPARTMENT of Commerce

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