Exploratory Precipitation Metrics: Spatiotemporal Characteristics, Process-Oriented, and Phenomena-Based (original) (raw)

Beyond the Basics: Evaluating Model-Based Precipitation Forecasts Using Traditional, Spatial, and Object-Based Methods

Weather and Forecasting, 2014

While traditional verification methods are commonly used to assess numerical model quantitative precipitation forecasts (QPFs) using a grid-to-grid approach, they generally offer little diagnostic information or reasoning behind the computed statistic. On the other hand, advanced spatial verification techniques, such as neighborhood and object-based methods, can provide more meaningful insight into differences between forecast and observed features in terms of skill with spatial scale, coverage area, displacement, orientation, and intensity. To demonstrate the utility of applying advanced verification techniques to mid- and coarse-resolution models, the Developmental Testbed Center (DTC) applied several traditional metrics and spatial verification techniques to QPFs provided by the Global Forecast System (GFS) and operational North American Mesoscale Model (NAM). Along with frequency bias and Gilbert skill score (GSS) adjusted for bias, both the fractions skill score (FSS) and Metho...

Statistical properties and validation of Quantitative Precipitation Forecast

2010

Observed precipitation fields show a high variability both in space and time and the amount of rainfall could vary a lot within a short distance.(Zepeda-Arce et al.,2000). The increasing of horizontal resolution in NWP models seems to enable them to reproduce this variability, even if frequent errors in time and space positioning make difficult a grid-point based employment of models QPF. In order to asses the ability of the models in reproducing the variability of the precipitation fields, we investigated the statistical properties of the observed and forecasted rain values falling within a predefined geographical area and in a specific time period (also called boxes). In particular we studied the distribution function (pdf )and evaluated some summarizing quantities, such as the mean, the maximum value and quantiles for each of the selected box. Results for different size of the chosen areas and period of time are used to validate the QPF of the COSMO suites that run operationally ...

A Survey of Precipitation Data for Environmental Modeling

2018

There is always a challenge of obtaining the “best” data to inform environmental models. Here we present different types of available precipitation datasets while detailing temporal and spatial resolution, potential errors in the dataset, and optimal performance scenarios. Our goal is to inform modelers of the various types, resolutions, and sources of precipitation data available for environmental modeling. Precipitation is the main driver in the hydrological cycle and modelers use this information to understand water quality and water availability. Environmental models use observed precipitation information for modeling past or current conditions, while simulated data are used to predict future conditions as well as recreate historic conditions. Several precipitation datasets and data generation methods such as National Climatic Data Center (NCDC) rain gauges, National and Global Land Data Assimilation (NLDAS, GLDAS), Next Generation Weather Radar, and Stochastic Weather Generator...

ALTERNATIVE MODELS IN PRECIPITATION ANALYSIS

emis.ams.org

Precipitation time series intrinsically contain important information concerning climate variability and change. Well-fit models of such time series can shed light upon past weather related phenomena and can help to explain future events. The objective of this study is to investi- ...

Relative Performance of Empirical Predictors of Daily Precipitation

2014

Abstract: The urgent need for realistic regional climate change scenarios has led to a plethora of empirical downscaling techniques. In many cases widely differing predictors are used, making comparative evaluation difficult. Additionally, it is not clear that the chosen predictors are always the most important. These limitations and the lack of physics in empirical downscaling highlight the need for a systematic assessment of the performance of physically meaningful predictors and their relevance in surface climate parameters. Accordingly, the objectives of this study are twofold: To examine the skill and errors of 29 individual atmospheric predictors of area-averaged daily precipitation in 15 locations that encompass a wide variety of climate regimes, and to determine the best combination of these to empirically model daily precipitation during the winter and summer seasons. The atmospheric predictors utilized in this study are from the National Center for Environmental Prediction...

Climate Models and Their Simulation of Precipitation

Energy & Environment, 2014

Current state-of-the-art General Circulation Models (GCMs) do not simulate precipitation well because they do not include the full range of precipitationforming mechanisms that occur in the real world. It is demonstrated here that the impact of these errors are not trivial-an error of only 1 mm in simulating liquid rainfall is equivalent to the energy required to heat the entire troposphere by 0.3°C. Given that models exhibit differences between the observed and modeled precipitation that often exceed 1 mm day-1 , this lost energy is not trivial. Thus, models and their prognostications are largely unreliable.

Precipitation Forecasts

2016

Abstract. The objective of this study is the scale dependent evaluation of precipitation forecasts of the Lokal-Modell (LM) from the German Weather Service in relation to dy-namical and cloud parameters. For this purpose the newly de-signed Dynamic State Index (DSI) is correlated with clouds and precipitation. The DSI quantitatively describes the devi-ation and relative distance from a stationary and adiabatic so-lution of the primitive equations. A case study and statistical analysis of clouds and precipitation demonstrates the avail-ability of the DSI as a dynamical threshold parameter. This confirms the importance of imbalances of the atmospheric flow field, which dynamically induce the generation of rain-fall. 1

Precipitation: Measurement, remote sensing, climatology and modeling

This review paper deals with four aspects of precipitation: measurement, remote sensing, climatology and modeling. The measurement of precipitation is summarized in terms of the instruments that count and measure drop sizes (defined as disdrometers) and the instruments that measure an average quantity proportional to the integrated volume of an ensemble of raindrops (these instruments are normally called rain gauges). Remote sensing of precipitation is accomplished with ground based radar and from satellite retrievals and these two approaches are separately discussed. The climatology of precipitation has evolved through the years from the traditional rain gauge data analyses to the more sophisticated data bases that result from a coalescence of data and information on precipitation that is available from several sources into amalgamated products. Recently, rain observations from both ground and space have been assimilated into regional and global numerical weather prediction models aiming at improved moisture analysis and better forecasts of extreme weather events. The current status and the main outstanding issues related to precipitation forecasting are discussed, providing a basic structure for research coordination aimed at the improvement of modeling, observation and data assimilation applicable to global and regional scales.

Quantitative Precipitation Forecasting: Report of the Eighth Prospectus Development Team, U.S. Weather Research Program

Bulletin of the American Meteorological Society, 1998

Quantitative precipitation forecasting (QPF) is the most important and significant challenge of weather forecasting. Advances in computing and observational technology combined with theoretical advances regarding the chaotic nature of the atmosphere offer the possibility of significant improvement in QPF. To achieve these improvements, this report recommends research focusing on 1) improving the accuracy and temporal and spatial resolution of the rainfall observing system; 2) performing process and climatological studies using the modernized observing system; 3) designing new data-gathering strategies for numerical model initialization; and 4) defining a probabilistic framework for precipitation forecasting and verification. Advances on the QPF problem will require development of advanced ensemble techniques that account for forecast uncertainty, stemming from sampling error and differences in model physics and numerics and development of statistical techniques for using observational data to verify probabilistic QPF in a way that is consistent with the chaotic nature of the precipitation process.