The accuracy of daily rainfall estimates from satellite observations and short-range numerical model forecasts is complementary, with satellite estimates performing best in summer and models in winter A ccurate measurements of precipitation on a variety of space and time scales are important not only to weather forecasters and climate scientists, but also to a wide range of decision makers, including hydrologists, agriculturalists, emergency managers, and industrialists. Precipitation measurements provide essential information about the global water cycle and the distribution of the Earth's latent AMERICAN METEOROLOGICAL SOCIETY heating, which has direct effects on the planetary circulation of the atmosphere. However, the historical record of precipitation observations is limited mostly to land areas where rain gauges can be deployed, and measurements from those instruments are sparse over large and meteorologically important regions of the earth, such as over the Amazon and equatorial Africa. Furthermore, precipitation observations over the oceans are limited over most of the historical record to scattered islands and, except for low-lying atolls, the observations from those locations are often not representative of the open ocean because the islands themselves have strong local influences on the precipitation.With the advent of meteorological satellites in the 1970s, scientists developed techniques to estimate precipitation from radiometric observations from satellites, which provide coverage over most of the globe. The first techniques used visible or infrared data to infer precipitation intensity based on the reflectivity of clouds (visible) and from cloud-top temperature (infrared). Both of those types of techniques yield crude estimates of precipitation because the link between cloud properties and precipitation JANUARY 2007 BAPI S" | 47

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A one‐dimensional thermodynamic model of sea ice is presented that focuses on those features that are most relevant to interactions with the atmosphere, namely the surface albedo and leads. It includes a surface albedo parameterization that interacts strongly with the state of the surface, and explicitly includes meltwater ponds. The lead parameterization contains a minimum lead fraction, absorption of solar radiation in and below the leads, lateral accretion and ablation of the sea ice, and a prescribed sea ice divergence rate. The model performed well in predicting the current climatic sea ice conditions in the central Arctic when compared with observations and other theoretical calculations. Results of parameter sensitivity tests produced large equilibrium ice thicknesses for small values of ice divergence or large values of minimum lead fraction as a result of positive feedback mechanisms involving cooling of water in the leads. The ice thickness was also quite sensitive to the meltwater runoff fraction and moderately sensitive to the other parameters in the melt pond parameterization, a result of the strong dependence of the surface albedo, and hence the net flux, on the surface conditions. To further investigate the physical interactions and internal feedback processes governing the sea ice‐lead system, sensitivity tests were also performed for each of the external forcing variables. The model's equilibrium sea ice thickness was extremely sensitive to changes in the downward longwave and shortwave fluxes and atmospheric temperature and humidity, moderately sensitive to the value of the ocean heat flux, and insensitive to values of wind speed, snowfall, and rainfall in the immediate vicinity of the baseline forcing, although significant changes in thickness occurred for larger variations in wind speed and snowfall. Four important positive feedback loops were identified and described: (1) the surface albedo feedback, (2) the conduction feedback, (3) the lead solar flux feedback, and (4) the lead fraction feedback. The destabilizing effects of these positive feedbacks were mitigated by two strong negative feedbacks: (1) the outgoing longwave flux feedback, and (2) the turbulent flux feedback. Considering the strong influence which sea ice has on global atmospheric and oceanic circulation patterns, it is essential that climate models be able to treat these feedback processes appropriately.

High-resolution forecasts from numerical models can look quite realistic and provide the forecaster with very useful guidance. However, when verified using traditional metrics they often score quite poorly because of the difficulty of predicting an exact match to the observations at high resolution. 'Fuzzy' verification rewards closeness by relaxing the requirement for exact matches between forecasts and observations. The key to the fuzzy approach is the use of a spatial window or neighbourhood surrounding the forecast and/or observed points. The treatment of the data within the window may include averaging (upscaling), thresholding, or generation of a PDF, depending on the particular fuzzy method used and its implicit decision model concerning what makes a good forecast. The size of the neighbourhood can be varied to provide verification results at multiple scales, thus allowing the user to determine at which scales the forecast has useful skill.This article describes a framework for fuzzy verification that incorporates several fuzzy verification methods. It is demonstrated on a high-resolution precipitation forecast from the United Kingdom (UK) and the results interpreted to show the additional information that can be gleaned from this approach.

Advancements in weather forecast models and their enhanced resolution have led to substantially improved and more realistic-appearing forecasts for some variables. However, traditional verification scores often indicate poor performance because of the increased small-scale variability so that the true quality of the forecasts is not always characterized well. As a result, numerous new methods for verifying these forecasts have been proposed. These new methods can mostly be classified into two overall categories: filtering methods and displacement methods. The filtering methods can be further delineated into neighborhood and scale separation, and the displacement methods can be divided into features based and field deformation. Each method gives considerably more information than the traditional scores, but it is not clear which method(s) should be used for which purpose.A verification methods intercomparison project has been established in order to glean a better understanding of the proposed methods in terms of their various characteristics and to determine what verification questions each method addresses. The study is ongoing, and preliminary qualitative results for the different approaches applied to different situations are described here. In particular, the various methods and their basic characteristics, similarities, and differences are described. In addition, several questions are addressed regarding the application of the methods and the information that they provide. These questions include (i) how the method(s) inform performance at different scales; (ii) how the methods provide information on location errors; (iii) whether the methods provide information on intensity errors and distributions; (iv) whether the methods provide information on structure errors; (v) whether the approaches have the ability to provide information about hits, misses, and false alarms; (vi) whether the methods do anything that is counterintuitive; (vii) whether the methods have selectable parameters and how sensitive the results are to parameter selection; (viii) whether the results can be easily aggregated across multiple cases; (ix) whether the methods can identify timing errors; and (x) whether confidence intervals and hypothesis tests can be readily computed.