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David J. Lorenz
,
Jason A. Otkin
,
Benjamin Zaitchik
,
Christopher Hain
, and
Martha C. Anderson

Abstract

Probabilistic forecasts of changes in soil moisture and an evaporative stress index (ESI) on subseasonal time scales over the contiguous United States are developed. The forecasts use the current land surface conditions and numerical weather prediction forecasts from the Subseasonal to Seasonal (S2S) Prediction project. Changes in soil moisture are quite predictable 8–14 days in advance with 50% or more of the variance explained over the majority of the contiguous United States; however, changes in ESI are significantly less predictable. A simple red noise model of predictability shows that the spatial variations in forecast skill are primarily a result of variations in the autocorrelation, or persistence, of the predicted variable, especially for the ESI. The difference in overall skill between soil moisture and ESI, on the other hand, is due to the greater soil moisture predictability by the numerical model forecasts. As the forecast lead time increases from 8–14 to 15–28 days, however, the autocorrelation dominates the soil moisture and ESI differences as well. An analysis of modeled transpiration, and bare soil and canopy water evaporation contributions to total evaporation, suggests improvements to the ESI forecasts can be achieved by estimating the relative contributions of these components to the initial ESI state. The importance of probabilistic forecasts for reproducing the correct probability of anomaly intensification is also shown.

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Ting-Chi Wu
,
Hui Liu
,
Sharanya J. Majumdar
,
Christopher S. Velden
, and
Jeffrey L. Anderson

Abstract

The influence of assimilating enhanced atmospheric motion vectors (AMVs) on mesoscale analyses and forecasts of tropical cyclones (TC) is investigated. AMVs from the geostationary Multifunctional Transport Satellite (MTSAT) are processed by the Cooperative Institute for Meteorological Satellite Studies (CIMSS, University of Wisconsin–Madison) for the duration of Typhoon Sinlaku (2008), which included a rapid intensification phase and a slow, meandering track. The ensemble Kalman filter and the Weather Research and Forecasting Model are utilized within the Data Assimilation Research Testbed. In addition to conventional observations, three different groups of AMVs are assimilated in parallel experiments: CTL, the same dataset assimilated in the NCEP operational analysis; CIMSS(h), hourly datasets processed by CIMSS; and CIMSS(h+RS), the dataset including AMVs from the rapid-scan mode. With an order of magnitude more AMV data assimilated, the CIMSS(h) analyses exhibit a superior track, intensity, and structure to CTL analyses. The corresponding 3-day ensemble forecasts initialized with CIMSS(h) yield smaller track and intensity errors than those initialized with CTL. During the period when rapid-scan AMVs are available, the CIMSS(h+RS) analyses offer additional modifications to the TC and its environment. In contrast to many members in the ensemble forecasts initialized from the CTL and CIMSS(h) analyses that predict an erroneous landfall in China, the CIMSS(h+RS) members capture recurvature, albeit prematurely. The results demonstrate the promise of assimilating enhanced AMV data into regional TC models. Further studies to identify optimal strategies for assimilating integrated full-resolution multivariate data from satellites are under way.

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Ting-Chi Wu
,
Christopher S. Velden
,
Sharanya J. Majumdar
,
Hui Liu
, and
Jeffrey L. Anderson

Abstract

Recent studies have shown that assimilating enhanced satellite-derived atmospheric motion vectors (AMVs) has improved mesoscale forecast of tropical cyclones (TC) track and intensity. The authors conduct data-denial experiments to understand where the TC analyses and forecasts benefit the most from the enhanced AMV information using an ensemble Kalman filter and the Weather Research and Forecasting Model. The Cooperative Institute for Meteorological Satellite Studies at the University of Wisconsin provides enhanced AMV datasets with higher density and temporal resolution using shorter-interval image triplets for the duration of Typhoon Sinlaku and Hurricane Ike (both 2008). These AMV datasets are then spatially and vertically subsetted to create six parallel cycled assimilation-forecast experiments for each TC: all AMVs; AMVs withheld between 100 and 350 hPa (upper layer), between 350 and 700 hPa (middle layer), and between 700 and 999 hPa (lower layer); and only AMVs within (interior) and outside (exterior) 1000-km radius of the TC center. All AMV subsets are found to be useful in some capacity. The interior and upper-layer AMVs are particularly crucial for improving initial TC position, intensity, and the three-dimensional wind structure along with their forecasts. Compared with denying interior or exterior AMVs, withholding AMVs in different tropospheric layers had less impact on TC intensity and size forecasts. The ensemble forecast is less certain (larger spread) in providing accurate TC track, intensity, and size when upper-layer AMVs or interior AMVs are withheld. This information could be useful to potential targeting scenarios, such as activating and focusing satellite rapid-scan operations, and decisions regarding observing system assessments and deployments.

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Huiling Yuan
,
Chungu Lu
,
John A. McGinley
,
Paul J. Schultz
,
Brian D. Jamison
,
Linda Wharton
, and
Christopher J. Anderson

Abstract

Short-range quantitative precipitation forecasts (QPFs) and probabilistic QPFs (PQPFs) are investigated for a time-lagged multimodel ensemble forecast system. One of the advantages of such an ensemble forecast system is its low-cost generation of ensemble members. In conjunction with a frequently cycling data assimilation system using a diabatic initialization [such as the Local Analysis and Prediction System (LAPS)], the time-lagged multimodel ensemble system offers a particularly appealing approach for QPF and PQPF applications. Using the NCEP stage IV precipitation analyses for verification, 6-h QPFs and PQPFs from this system are assessed during the period of March–May 2005 over the west-central United States. The ensemble system was initialized by hourly LAPS runs at a horizontal resolution of 12 km using two mesoscale models, including the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) and the Weather Research and Forecast (WRF) model with the Advanced Research WRF (ARW) dynamic core. The 6-h PQPFs from this system provide better performance than the NCEP operational North American Mesoscale (NAM) deterministic runs at 12-km resolution, even though individual members of the MM5 or WRF models perform comparatively worse than the NAM forecasts at higher thresholds and longer lead times. Recalibration was conducted to reduce the intensity errors in time-lagged members. In spite of large biases and spatial displacement errors in the MM5 and WRF forecasts, statistical verification of QPFs and PQPFs shows more skill at longer lead times by adding more members from earlier initialized forecast cycles. Combing the two models only reduced the forecast biases. The results suggest that further studies on time-lagged multimodel ensembles for operational forecasts are needed.

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Michael T. Hobbins
,
Andrew Wood
,
Daniel J. McEvoy
,
Justin L. Huntington
,
Charles Morton
,
Martha Anderson
, and
Christopher Hain

Abstract

Many operational drought indices focus primarily on precipitation and temperature when depicting hydroclimatic anomalies, and this perspective can be augmented by analyses and products that reflect the evaporative dynamics of drought. The linkage between atmospheric evaporative demand E 0 and actual evapotranspiration (ET) is leveraged in a new drought index based solely on E 0—the Evaporative Demand Drought Index (EDDI). EDDI measures the signal of drought through the response of E 0 to surface drying anomalies that result from two distinct land surface–atmosphere interactions: 1) a complementary relationship between E 0 and ET that develops under moisture limitations at the land surface, leading to ET declining and increasing E 0, as in sustained droughts, and 2) parallel ET and E 0 increases arising from increased energy availability that lead to surface moisture limitations, as in flash droughts. To calculate EDDI from E 0, a long-term, daily reanalysis of reference ET estimated from the American Society of Civil Engineers (ASCE) standardized reference ET equation using radiation and meteorological variables from the North American Land Data Assimilation System phase 2 (NLDAS-2) is used. EDDI is obtained by deriving empirical probabilities of aggregated E 0 depths relative to their climatologic means across a user-specific time period and normalizing these probabilities. Positive EDDI values then indicate drier-than-normal conditions and the potential for drought. EDDI is a physically based, multiscalar drought index that that can serve as an indicator of both flash and sustained droughts, in some hydroclimates offering early warning relative to current operational drought indices. The performance of EDDI is assessed against other commonly used drought metrics across CONUS in Part II.

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Daniel J. McEvoy
,
Justin L. Huntington
,
Michael T. Hobbins
,
Andrew Wood
,
Charles Morton
,
Martha Anderson
, and
Christopher Hain

Abstract

Precipitation, soil moisture, and air temperature are the most commonly used climate variables to monitor drought; however, other climatic factors such as solar radiation, wind speed, and humidity can be important drivers in the depletion of soil moisture and evolution and persistence of drought. This work assesses the Evaporative Demand Drought Index (EDDI) at multiple time scales for several hydroclimates as the second part of a two-part study. EDDI and individual evaporative demand components were examined as they relate to the dynamic evolution of flash drought over the central United States, characterization of hydrologic drought over the western United States, and comparison to commonly used drought metrics of the U.S. Drought Monitor (USDM), Standardized Precipitation Index (SPI), Standardized Soil Moisture Index (SSI), and the evaporative stress index (ESI). Two main advantages of EDDI over other drought indices are that it is independent of precipitation (similar to ESI) and it can be decomposed to identify the role individual evaporative drivers have on drought onset and persistence. At short time scales, spatial distributions and time series results illustrate that EDDI often indicates drought onset well in advance of the USDM, SPI, and SSI. Results illustrate the benefits of physically based evaporative demand estimates and demonstrate EDDI’s utility and effectiveness in an easy-to-implement agricultural early warning and long-term hydrologic drought–monitoring tool with potential applications in seasonal forecasting and fire-weather monitoring.

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David J. Lorenz
,
Jason A. Otkin
,
Mark Svoboda
,
Christopher R. Hain
,
Martha C. Anderson
, and
Yafang Zhong

Abstract

The U.S. Drought Monitor (USDM) classifies drought into five discrete dryness/drought categories based on expert synthesis of numerous data sources. In this study, an empirical methodology is presented for creating a nondiscrete USDM index that simultaneously 1) represents the dryness/wetness value on a continuum and 2) is most consistent with the time scales and processes of the actual USDM. A continuous USDM representation will facilitate USDM forecasting methods, which will benefit from knowledge of where, within a discrete drought class, the current drought state most probably lies. The continuous USDM is developed such that the actual discrete USDM can be reconstructed by discretizing the continuous USDM based on the 30th, 20th, 10th, 5th, and 2nd percentiles—corresponding with USDM definitions for the D4–D0 drought classes. Anomalies in precipitation, soil moisture, and evapotranspiration over a range of different time scales are used as predictors to estimate the continuous USDM. The methodology is fundamentally probabilistic, meaning that the probability density function (PDF) of the continuous USDM is estimated and therefore the degree of uncertainty in the fit is properly characterized. Goodness-of-fit metrics and direct comparisons between the actual and predicted USDM analyses during different seasons and years indicate that this objective drought classification method is well correlated with the current USDM analyses. In Part II, this continuous USDM index will be used to improve intraseasonal USDM intensification forecasts because it is capable of distinguishing between USDM states that are either far from or near to the next-higher drought category.

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David J. Lorenz
,
Jason A. Otkin
,
Mark Svoboda
,
Christopher R. Hain
,
Martha C. Anderson
, and
Yafang Zhong

Abstract

Probabilistic forecasts of U.S. Drought Monitor (USDM) intensification over 2-, 4-, and 8-week time periods are developed based on recent anomalies in precipitation, evapotranspiration, and soil moisture. These statistical forecasts are computed using logistic regression with cross validation. While recent precipitation, evapotranspiration, and soil moisture do provide skillful forecasts, it is found that additional information on the current state of the USDM adds significant skill to the forecasts. The USDM state information takes the form of a metric that quantifies the “distance” from the next-higher drought category using a nondiscrete estimate of the current USDM state. This adds skill because USDM states that are close to the next-higher drought category are more likely to intensify than states that are farther from this threshold. The method shows skill over most of the United States but is most skillful over the north-central United States, where the cross-validated Brier skill score averages 0.20 for both 2- and 4-week forecasts. The 8-week forecasts are less skillful in most locations. The 2- and 4-week probabilities have very good reliability. The 8-week probabilities, on the other hand, are noticeably overconfident. For individual drought events, the method shows the most skill when forecasting high-amplitude flash droughts and when large regions of the United States are experiencing intensifying drought.

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Wiebe A. Oost
,
Christopher W. Fairall
,
James B. Edson
,
Stuart D. Smith
,
Robert J. Anderson
,
John A.B. Wills
,
Kristina B. Katsaros
, and
Janice DeCosmo

Abstract

Several methods are examined for correction of turbulence and eddy fluxes in the atmospheric boundary layer, two of them based on a potential-flow approach initiated by Wyngaard. If the distorting object is cylindrical or if the distance to the sensor is much greater than the size of the body, the undisturbed wind stress can be calculated solely from measurements made by the sensor itself; no auxiliary measurements or lengthy model calculations are needed. A more general potential-flow correction has been developed in which distorting objects of complex shape are represented as a number of ellipsoidal elements.

These models are applied to data from three turbulence anemometers with differing amounts of flow distortion, operated simultaneously in the Humidity Exchange over the Sea (HEXOS) Main Experiment. The results are compared with wind-stress estimates by the inertial-dissipation technique; these are much less sensitive to local flow distortion and are consistent with the corrected eddy correlation results. From these comparisons it is concluded that the commonly used “tilt correction” is not sufficient to correct eddy wind stress for distortion by nearby objects, such as probe supports and neighboring sensors.

Neither potential-flow method is applicable to distortion by larger bodies of a scale comparable to the measuring height, such as the superstructure of the Meetpost Noordwijk (MPN) platform used in HEXOS. Flow distortion has been measured around a model of MPN in a wind tunnel study. The results were used to correct mean winds, but simulation of distortion effects on turbulence levels and wind stress turned out not to be feasible.

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David J. Lorenz
,
Jason A. Otkin
,
Benjamin F. Zaitchik
,
Christopher Hain
,
Thomas R. H. Holmes
, and
Martha C. Anderson

Abstract

The effect of machine learning and other enhancements on statistical-dynamical forecasts of soil moisture (0-10cm and 0-100cm) and a reference evapotranspiration fraction (Evaporative Stress Index, ESI) on sub-seasonal time scales (15-28 days) are explored. The predictors include the current and past land surface conditions, and dynamical model hindcasts from the Sub-seasonal to Seasonal (S2S) Prediction Project. When the methods are enhanced with machine learning and other improvements, the increases in skill are almost exclusively coming from predictors drawn from observations of current and past land surface states. This suggests that operational S2S flash drought forecasts should focus on optimizing use of information on current conditions rather than on integrating dynamically based forecasts, given the current state of knowledge. Nonlinear machine learning methods lead to improved skill over linear methods for soil moisture but not for ESI. Improvements for both soil moisture and ESI are realized by increasing the sample size by including surrounding grid points in training and increasing the number of predictors. In addition, all the improvements in the soil moisture forecasts predominantly impact soil moistening rather than soil drying—i.e., prediction of conditions moving away from drought rather than into drought—especially when the initial soil state is drier than normal. The physical reasons for the nonlinear machine learning improvements are also explored.

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