A Viking Lander 1 image was modeled as mixtures of reflectance spectra of palagonite dust, gray andesitelike rock, and a coarse rocklike soil. The rocks are covered to varying degrees by dust but otherwise appear unweathered. Rocklike soil occurs as lag deposits in deflation zones around stones and on top of a drift and as a layer in a trench dug by the lander. This soil probably is derived from the rocks by wind abrasion and/or spallation. Dust is the major component of the soil and covers most of the surface. The dust is unrelated spectrally to the rock but is equivalent to the global-scale dust observed telescopically. A new method was developed to model a multispectral image as mixtures of end-member spectra and to compare image spectra directly with laboratory reference spectra. The method for the first time uses shade and secondary illumination effects as spectral end-members; thus the effects of topography and illumination on all scales can be isolated or removed. The image was calibrated absolutely from the laboratory spectra, in close agreement with direct calibrations. The method has broad applications to interpreting multispectral images, including satellite images.

Spectral mixture modeling - A new analysis of rock and soil types at the Viking Lander 1 site. [on Mars]

https://meetingorganizer.copernicus.org/EGU2010/EGU2010-15247.pdf

Ensemble flood forecasting: a review.

Ensemble forecasting

A New York Times Bestseller An Economist Best Book of 2015 "The most important book on decision making since Daniel Kahneman's Thinking, Fast and Slow." Jason Zweig, The Wall Street Journal Everyone would benefit from seeing further into the future, whether buying stocks, crafting policy, launching a new product, or simply planning the weeks meals. Unfortunately, people tend to be terrible forecasters. As Wharton professor Philip Tetlock showed in a landmark 2005 study, even experts predictions are only slightly better than chance. However, an important and underreported conclusion of that study was that some experts do have real foresight, and Tetlock has spent the past decade trying to figure out why. What makes some people so good? And can this talent be taught? In Superforecasting, Tetlock and coauthor Dan Gardner offer a masterwork on prediction, drawing on decades of research and the results of a massive, government-funded forecasting tournament. The Good Judgment Project involves tens of thousands of ordinary peopleincluding a Brooklyn filmmaker, a retired pipe installer, and a former ballroom dancerwho set out to forecast global events. Some of the volunteers have turned out to be astonishingly good. Theyve beaten other benchmarks, competitors, and prediction markets. Theyve even beaten the collective judgment of intelligence analysts with access to classified information. They are "superforecasters." In this groundbreaking and accessible book, Tetlock and Gardner show us how we can learn from this elite group. Weaving together stories of forecasting successes (the raid on Osama bin Ladens compound) and failures (the Bay of Pigs) and interviews with a range of high-level decision makers, from David Petraeus to Robert Rubin, they show that good forecasting doesnt require powerful computers or arcane methods. It involves gathering evidence from a variety of sources, thinking probabilistically, working in teams, keeping score, and being willing to admit error and change course. Superforecasting offers the first demonstrably effective way to improve our ability to predict the futurewhether in business, finance, politics, international affairs, or daily lifeand is destined to become a modern classic.

Superforecasting: The Art and Science of Prediction

Forecast quality is evaluated through examination and manipulation of the joint frequency distribution of forecasts and their corresponding observations. Full examination of this distribution allows diagnosis of various strengths and weaknesses in a set of forecasts. The various available scalar summary measures are convenient simplifications but inevitably discard some information.

Chapter 8 - Forecast Verification

. Ensemble prediction systems are used operationally to make probabilistic streamflow forecasts for seasonal time scales. However, hydrological models used for ensemble streamflow prediction often have simulation biases that degrade forecast quality and limit the operational usefulness of the forecasts. This study evaluates three bias-correction methods for ensemble streamflow volume forecasts. All three adjust the ensemble traces using a transformation derived with simulated and observed flows from a historical simulation. The quality of probabilistic forecasts issued when using the three bias-correction methods is evaluated using a distributions-oriented verification approach. Comparisons are made of retrospective forecasts of monthly flow volumes for a north-central United States basin (Des Moines River, Iowa), issued sequentially for each month over a 48-year record. The results show that all three bias-correction methods significantly improve forecast quality by eliminating unconditional biases and enhancing the potential skill. Still, subtle differences in the attributes of the bias-corrected forecasts have important implications for their use in operational decision-making. Diagnostic verification distinguishes these attributes in a context meaningful for decision-making, providing criteria to choose among bias-correction methods with comparable skill.

/pdf/evaluation-of-bias-correction-methods-for-ensemble-44hn1rquz5.pdf

Evaluation of bias-correction methods for ensemble streamflow volume forecasts

For probability forecasts, the Brier score and Brier skill score are commonly used verification measures of forecast accuracy and skill. Using sampling theory, analytical expressions are derived to estimate their sampling uncertainties. The Brier score is an unbiased estimator of the accuracy, and an exact expression defines its sampling variance. The Brier skill score (with climatology as a reference forecast) is a biased estimator, and approximations are needed to estimate its bias and sampling variance. The uncertainty estimators depend only on the moments of the forecasts and observations, so it is easy to routinely compute them at the same time as the Brier score and skill score. The resulting uncertainty estimates can be used to construct error bars or confidence intervals for the verification measures, or perform hypothesis testing. Monte Carlo experiments using synthetic forecasting examples illustrate the performance of the expressions. In general, the estimates provide very reliable inf...

Sampling Uncertainty and Confidence Intervals for the Brier Score and Brier Skill Score

The effective use of pervious concrete in environmental site design requires consistent design procedures integrating the structural and material properties of the pervious concrete pavement with hydrologic performance of the pervious concrete system. Design procedures to size pervious concrete storm-water systems are presented based on criteria for freeze-thaw protection and drawdown reliability. Hydrologic performance criteria are quantified by an effective curve number, estimated from simulated routing of design storm hydrographs using standard storm-water computations. Combining operational design criteria with the evaluation of hydrologic perfor- mance criteria, as an effective curve number, integrates pervious concrete systems with traditional storm-water management practice and emerging standards for environmental site design.

Effective Curve Number and Hydrologic Design of Pervious Concrete Storm-Water Systems

Ensemble prediction systems produce forecasts that represent the probability distribution of a continuous forecast variable. Most often, the verification problem is simplified by transforming the ensemble forecast into probability forecasts for discrete events, where the events are defined by one or more threshold values. Then, skill is evaluated using the mean-square error (MSE; i.e., Brier) skill score for binary events, or the ranked probability skill score (RPSS) for multicategory events. A framework is introduced that generalizes this approach, by describing the forecast quality of ensemble forecasts as a continuous function of the threshold value. Viewing ensemble forecast quality this way leads to the interpretation of the RPSS and the continuous ranked probability skill score (CRPSS) as measures of the weighted-average skill over the threshold values. It also motivates additional measures, derived to summarize other features of a continuous forecast quality function, which can be interpret...

Stuart S. Schwartz

Papers

Evaluation of bias-correction methods for ensemble streamflow volume forecasts

Sampling Uncertainty and Confidence Intervals for the Brier Score and Brier Skill Score

Effective Curve Number and Hydrologic Design of Pervious Concrete Storm-Water Systems

Summary Verification Measures and Their Interpretation for Ensemble Forecasts

Slowflow fingerprints of urban hydrology