The Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) provides a calibration-based sequential scheme for combining precipitation estimates from multiple satellites, as well as gauge analyses where feasible, at fine scales (0.25° × 0.25° and 3 hourly). TMPA is available both after and in real time, based on calibration by the TRMM Combined Instrument and TRMM Microwave Imager precipitation products, respectively. Only the after-real-time product incorporates gauge data at the present. The dataset covers the latitude band 50°N–S for the period from 1998 to the delayed present. Early validation results are as follows: the TMPA provides reasonable performance at monthly scales, although it is shown to have precipitation rate–dependent low bias due to lack of sensitivity to low precipitation rates over ocean in one of the input products [based on Advanced Microwave Sounding Unit-B (AMSU-B)]. At finer scales the TMPA is successful at approximately reproducing the s...

/pdf/the-trmm-multisatellite-precipitation-analysis-tmpa-quasi-3wulgfnahi.pdf

The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-Global, Multiyear, Combined-Sensor Precipitation Estimates at Fine Scales

Summary
1In recent years the use of species distribution models by ecologists and conservation managers has increased considerably, along with an awareness of the need to provide accuracy assessment for predictions of such models. The kappa statistic is the most widely used measure for the performance of models generating presence–absence predictions, but several studies have criticized it for being inherently dependent on prevalence, and argued that this dependency introduces statistical artefacts to estimates of predictive accuracy. This criticism has been supported recently by computer simulations showing that kappa responds to the prevalence of the modelled species in a unimodal fashion.
2In this paper we provide a theoretical explanation for the observed dependence of kappa on prevalence, and introduce into ecology an alternative measure of accuracy, the true skill statistic (TSS), which corrects for this dependence while still keeping all the advantages of kappa. We also compare the responses of kappa and TSS to prevalence using empirical data, by modelling distribution patterns of 128 species of woody plant in Israel.
3The theoretical analysis shows that kappa responds in a unimodal fashion to variation in prevalence and that the level of prevalence that maximizes kappa depends on the ratio between sensitivity (the proportion of correctly predicted presences) and specificity (the proportion of correctly predicted absences). In contrast, TSS is independent of prevalence.
4When the two measures of accuracy were compared using empirical data, kappa showed a unimodal response to prevalence, in agreement with the theoretical analysis. TSS showed a decreasing linear response to prevalence, a result we interpret as reflecting true ecological phenomena rather than a statistical artefact. This interpretation is supported by the fact that a similar pattern was found for the area under the ROC curve, a measure known to be independent of prevalence.
5Synthesis and applications. Our results provide theoretical and empirical evidence that kappa, one of the most widely used measures of model performance in ecology, has serious limitations that make it unsuitable for such applications. The alternative we suggest, TSS, compensates for the shortcomings of kappa while keeping all of its advantages. We therefore recommend the TSS as a simple and intuitive measure for the performance of species distribution models when predictions are expressed as presence–absence maps.

https://besjournals.onlinelibrary.wiley.com/doi/pdf/10.1111/j.1365-2664.2006.01214.x

Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS)

The JRA-55 Reanalysis: General Specifications and Basic Characteristics

Description of the NCAR Community Atmosphere Model (CAM 3.0)

This progress report on the International Satellite Cloud Climatology Project (ISCCP) describes changes made to produce new cloud data products (D data), examines the evidence that these changes are improvements over the previous version (C data), summarizes some results, and discusses plans for the ISCCP through 2005. By late 1999 all datasets will be available for the period from July 1983 through December 1997. The most significant changes in the new D-series cloud datasets are 1) revised radiance calibrations to remove spurious changes in the long-term record, 2) increased cirrus detection sensitivity over land, 3) increased low-level cloud detection sensitivity in polar regions, 4) reduced biases in cirrus cloud properties using an ice crystal microphysics model in place of a liquid droplet microphysics model, and 5) increased detail about the variations of cloud properties. The ISCCP calibrations are now the most complete and self-consistent set of calibrations available for all the weather...

http://ruby.fgcu.edu/courses/twimberley/EnviroPhilo/ISCCP.pdf

Advances in understanding clouds from ISCCP

An increasing number of satellite-based rainfall products are now available in near–real time over the Internet to help meet the needs of weather forecasters and climate scientists, as well as a wide range of decision makers, including hydrologists, agriculturalists, emergency managers, and industrialists. Many of these satellite products are so newly developed that a comprehensive evaluation has not yet been undertaken. This article provides potential users of short-interval satellite rainfall estimates with information on the accuracy of such estimates. Since late 2002 the authors have been performing daily validation and intercomparisons of several operational satellite rainfall retrieval algorithms over Australia, the United States, and northwestern Europe. Short-range quantitative precipitation forecasts from four numerical weather prediction (NWP) models are also included for comparison. Synthesis of four years of daily rainfall validation results shows that the satellite-derived estimates of precip...

Comparison of near-real-time precipitation estimates from satellite observations and numerical models

The sea ice-albedo feedback mechanism over the Arctic Ocean multiyear sea ice is investigated by conducting a series of experiments using several one-dimensional models of the coupled sea ice-atmosphere system. In its simplest form, ice-albedo feedback is thought to be associated with a decrease in the areal cover of snow and ice and a corresponding increase in the surface temperature, further decreasing the areal cover of snow and ice. It is shown that the sea ice-albedo feedback can operate even in multiyear pack ice, without the disappearance of this ice, associated with internal processes occurring within the multiyear ice pack (e.g., duration of the snow cover, ice thickness, ice distribution, lead fraction, and melt pond characteristics). The strength of the ice-albedo feedback mechanism is compared for several different thermodynamic sea ice models: a new model that includes ice thickness distribution, the Ebert and Curry model, the Maykut and Untersteiner model, and the Semtner level-3 an...

Sea Ice-Albedo Climate Feedback Mechanism

Verification of precipitation in weather systems: determination of systematic errors

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

An intermediate one‐dimensional thermodynamic sea ice model for investigating ice‐atmosphere interactions

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. Copyright © 2008 Royal Meteorological Society

https://rmets.onlinelibrary.wiley.com/doi/epdf/10.1002/met.25

Elizabeth E. Ebert

Papers

Comparison of near-real-time precipitation estimates from satellite observations and numerical models

Sea Ice-Albedo Climate Feedback Mechanism

Verification of precipitation in weather systems: determination of systematic errors

An intermediate one‐dimensional thermodynamic sea ice model for investigating ice‐atmosphere interactions

Fuzzy verification of high-resolution gridded forecasts: a review and proposed framework