Subhrendu Gangopadhyay

The Schaake Shuffle: A Method for Reconstructing Space–Time Variability in Forecasted Precipitation and Temperature Fields

Abstract A number of statistical methods that are used to provide local-scale ensemble forecasts of precipitation and temperature do not contain realistic spatial covariability between neighboring stations or realistic temporal persistence for subsequent forecast lead times. To demonstrate this point, output from a global-scale numerical weather prediction model is used in a stepwise multiple linear regression approach to downscale precipitation and temperature to individual stations located in and around four study basins in the United States. Output from the forecast model is downscaled for lead times up to 14 days. Residuals in the regression equation are modeled stochastically to provide 100 ensemble forecasts. The precipitation and temperature ensembles from this approach have a poor representation of the spatial variability and temporal persistence. The spatial correlations for downscaled output are considerably lower than observed spatial correlations at short forecast lead times (e.g., less than 5...

Climate Index Weighting Schemes for NWS ESP-Based Seasonal Volume Forecasts

This study compares methods to incorporate climate information into the National Weather Service River Forecast System (NWSRFS). Three small-to-medium river subbasins following roughly along a longitude in the Colorado River basin with different El Nino-Southern Oscillation signals were chosen as test basins. Historical ensemble forecasts of the spring runoff for each basin were generated using modeled hydrologic states and historical precipitation and temperature observations using the Ensemble Streamflow Prediction (ESP) component of the NWSRFS. Two general methods for using a climate index (e.g., Nino-3.4) are presented. The first method, post-ESP, uses the climate index to weight ensemble members from ESP. Four different post-ESP weighting schemes are presented. The second method, preadjustment, uses the climate index to modify the temperature and precipitation ensembles used in ESP. Two preadjustment methods are presented. This study shows the distance-sensitive nearest-neighbor post-ESP to be superior to the other post-ESP weighting schemes. Further, for the basins studied, forecasts based on post-ESP techniques outperformed those based on preadjustment techniques. 1. Introduction and background Ensemble streamflow forecasts are made routinely by the National Weather Service (NWS) for seasonal river volumes using Ensemble Streamflow Prediction (ESP), which is a component of the NWS River Forecast Sys- tem (NWSRFS). ESP uses the current hydrologic model states as initial conditions and drives the model using historical temperature and precipitation (Day 1985). ESP produces a flow trace that corresponds to a partic- ular year of historical weather. Taken together, the en- semble of flow traces may be transformed into a prob- abilistic forecast for any future variable. Current NWS methodology allows a user to choose different methods for transforming the ensemble values into a probabilistic forecast. The ensemble values can be used to define an empirical probability distribution or in fitting a proba- bility distribution function (i.e., normal, weibull, etc.). However, used alone, this procedure does not account for any additional knowledge of the climate system,

Characterizing Uncertainty of the Hydrologic Impacts of Climate Change

The high climate sensitivity of hydrologic systems, the importance of those systems to society, and the imprecise nature of future climate projections all motivate interest in characterizing uncertainty in the hydrologic impacts of climate change. We discuss recent research that exposes important sources of uncertainty that are commonly neglected by the water management community, especially, uncertainties associated with internal climate system variability, and hydrologic modeling. We also discuss research exposing several issues with widely used climate downscaling methods. We propose that progress can be made following parallel paths: first, by explicitly characterizing the uncertainties throughout the modeling process (rather than using an ad hoc “ensemble of opportunity”) and second, by reducing uncertainties through developing criteria for excluding poor methods/models, as well as with targeted research to improve modeling capabilities. We argue that such research to reveal, reduce, and represent uncertainties is essential to establish a defensible range of quantitative hydrologic storylines of climate change impacts.

Incorporating Medium-Range Numerical Weather Model Output into the Ensemble Streamflow Prediction System of the National Weather Service

Abstract This study introduces medium-range meteorological ensemble inputs of temperature and precipitation into the Ensemble Streamflow Prediction component of the National Weather Service River Forecast System (NWSRFS). The Climate Diagnostics Center (CDC) produced a reforecast archive of model forecast runs from a dynamically frozen version of the Medium-Range Forecast (MRF) model. This archive was used to derive statistical relationships between MRF variables and historical basin-average precipitation and temperatures. The latter are used to feed the Ensemble Streamflow Prediction (ESP) component of the NWSRFS. Two sets of ESP reforecasts were produced: A control run based on historically observed temperature and precipitation and an experimental run based on MRF-derived temperature and precipitation. This study found the MRF reforecasts to be generally superior to the control reforecasts, although there were situations when the downscaled MRF output actually degraded the forecast. Forecast improvemen...

Hydrologic Implications of Different Large-Scale Meteorological Model Forcing Datasets in Mountainous Regions

AbstractProcess-based hydrologic models require extensive meteorological forcing data, including data on precipitation, temperature, shortwave and longwave radiation, humidity, surface pressure, and wind speed. Observations of precipitation and temperature are more common than other variables; consequently, radiation, humidity, pressure, and wind speed often must be either estimated using empirical relationships with precipitation and temperature or obtained from numerical weather prediction models. This study examines two climate forcing datasets using different methods to estimate radiative energy fluxes and humidity and investigates the effects of the choice of forcing data on hydrologic simulations over the mountainous upper Colorado River basin (293 472 km2). Comparisons of model simulations forced by two climate datasets illustrate that the methods used to estimate shortwave radiation impact hydrologic states and fluxes, particularly at high elevation (e.g., ~20% difference in runoff above 3000-m el...

Joint Spatiotemporal Variability of Global Sea Surface Temperatures and Global Palmer Drought Severity Index Values

Abstract Dominant modes of individual and joint variability in global sea surface temperatures (SST) and global Palmer drought severity index (PDSI) values for the twentieth century are identified through a multivariate frequency domain singular value decomposition. This analysis indicates that a secular trend and variability related to the El Nino–Southern Oscillation (ENSO) are the dominant modes of variance shared among the global datasets. For the SST data the secular trend corresponds to a positive trend in Indian Ocean and South Atlantic SSTs, and a negative trend in North Pacific and North Atlantic SSTs. The ENSO reconstruction shows a strong signal in the tropical Pacific, North Pacific, and Indian Ocean regions. For the PDSI data, the secular trend reconstruction shows high amplitudes over central Africa including the Sahel, whereas the regions with strong ENSO amplitudes in PDSI are the southwestern and northwestern United States, South Africa, northeastern Brazil, central Africa, the Indian sub...

How Does the Choice of Distributed Meteorological Data Affect Hydrologic Model Calibration and Streamflow Simulations

AbstractSpatially distributed historical meteorological forcings (temperature and precipitation) are commonly incorporated into modeling efforts for long-term natural resources planning. For water management decisions, it is critical to understand the uncertainty associated with the different choices made in hydrologic impact assessments (choice of hydrologic model, choice of forcing dataset, calibration strategy, etc.). This paper evaluates differences among four commonly used historical meteorological datasets and their impacts on streamflow simulations produced using the Variable Infiltration Capacity (VIC) model. The four meteorological datasets examined here have substantial differences, particularly in minimum and maximum temperatures in high-elevation regions such as the Rocky Mountains. The temperature differences among meteorological forcing datasets are generally larger than the differences between calibration and validation periods. Of the four meteorological forcing datasets considered, there ...

Effects of Spatial and Temporal Aggregation on the Accuracy of Statistically Downscaled Precipitation Estimates in the Upper Colorado River Basin

To test the accuracy of statistically downscaled precipitation estimates from numerical weather prediction models, a set of experiments to study what space and time scales are appropriate to obtain downscaled precipitation forecasts with maximum skill have been carried out. Fourteen-day forecasts from the 1998 version of the National Centers for Environmental Prediction (NCEP) Medium-Range Forecast (MRF) model were used in this study. It has been previously found that downscaled temperature fields have significant skill even up to 5 days of forecast lead time, but there is practically no valuable skill in the downscaled precipitation forecasts. Low skill in precipitation forecasts revolves around two main issues. First, the (intermittent) precipitation variability on daily and subdaily time scales could be too noisy to derive meaningful relationships with atmospheric predictors. Second, the model parameterizations and the coarse spatial resolution of the current generation of global-scale forecast models might be unable to resolve the local-scale variability in precipitation. Both of these issues may be addressed by spatial and temporal averaging. In this paper the authors present a diagnostic study using a set of numerical experiments to understand how spatial and temporal aggregations affect the skill of downscaled precipitation forecasts in the upper Colorado River basin. The question addressed is, if the same set of predictor variables from numerical weather prediction models is used, what space (e.g., station versus regional average) and time (e.g., subdaily versus daily) scales optimize regression-based downscaling models so as to maximize forecast skill for precipitation? Results in general show that spatial and temporal averaging increased the skill of downscaled precipitation estimates. At subdaily (6 hourly) and daily time scales, the skill of downscaled estimates at spatial scales greater than 50 km was generally higher than the skill of downscaled estimates at individual stations. For the 6-hourly time scale both for stations and for mean areal precipitation estimates the maximum forecast skill was found to be approximately half that of the daily time scale. At forecast lead times of 5 days, when there is very little skill at daily and subdaily time scales, useful skill emerged when station data are aggregated to 3- and 5-day averages.

Spatial variability of seasonal extreme precipitation in the western United States

We examine the characteristics of 3 day total extreme precipitation in the western United States. Coherent seasonal spatial patterns of timing and magnitude are evident in the data, motivating a seasonally based analysis. Using a clustering method that is consistent with extreme value theory, we identify coherent regions for extremes that vary seasonally. Based on storm back trajectory analysis, we demonstrate unique moisture sources and dominant moisture pathways for each spatial region. In the winter the Pacific Ocean is the dominant moisture source across the west, but in other seasons the Gulf of Mexico, the Gulf of California, and the land surface over the midwestern U.S. play an important role. We find the El Nino–Southern Oscillation (ENSO) to not have a strong impact on dominant moisture delivery pathways or moisture sources. The frequency of extremes under ENSO is spatially coherent and seasonally dependent with certain regions tending to have more (less) frequent extreme events in El Nino (La Nina) conditions.

Hydrologic projections for the western United States

Motivated by a common interest in establishing data access for climate change impacts analysis, the U.S. Department of the Interiors Bureau of Reclamation (referred to hereinafter as Reclamation) has collaborated since 2007 with federal and nonfederal entities to provide monthly gridded precipitation and temperature data from 112 contemporary climate projections (Coupled Model Intercomparison Project Phase 3 (CMIP3)) over the contiguous United States. The grid size resolution of this downscaled data archive (publicly available at http://gdo-dcp.ucllnl.org/downscaled_cmip3_projections/) is 1/8° latitude x 1/8° longitude (approximately 12 x 12 kilometers) and covers the period 1950–2099 [Maurer et al., 2007]. Downscaling is necessary to develop hydroclimate data (e.g., precipitation and temperature) from a coarse- resolution climate model grid to a higher-resolution grid, and the CMIP3 archive was downscaled using the statistical method of bias correction. Although approximately 1000 unique users to date have downloaded the precipitation and temperature information contained within the archive (commonly referred to as the bias corrected spatially downscaled, or BCSD-CMIP3, archive), these temperature and precipitation projections have not been used to consistently generate hydrologic projections over the United States and at fine enough scale to perform hydrologic impacts analysis and support local adaptation assessments. Without available hydrologic projections, planners typically develop and apply their own site-specific and local hydrology models to fill this information gap. However, this makes consistent regional intercomparisons of hydrologic impacts of climate change difficult.