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  • Author or Editor: Christopher J. Anderson x
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Christopher J. Anderson
,
Raymond W. Arritt
, and
John S. Kain

Abstract

The authors have altered the vertical profile of updraft mass flux detrainment in an implementation of the Kain–Fritsch2 (KF2) convective parameterization within the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (Penn State–NCAR) Mesoscale Model (MM5). The effect of this modification was to alter the vertical profile of convective parameterization cloud mass (including cloud water and ice) supplied to the host model for explicit simulation by the grid-resolved dynamical equations and parameterized microphysical processes. These modifications and their sensitivity to horizontal resolution in a matrix of experimental simulations of the June–July 1993 flood in the central United States were tested.

The KF2 modifications impacted the diurnal cycle of precipitation by reducing precipitation from the convective parameterization and increasing precipitation from more slowly evolving mesoscale processes. The modified KF2 reduced an afternoon bias of high precipitation rate in both low- and high-resolution simulations but affected mesoscale precipitation processes only in high-resolution simulations. The combination of high-resolution and modified KF2 resulted in more frequent and more realistically clustered propagating, nocturnal mesoscale precipitation events and agreed best with observations of the nocturnal precipitation rate.

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Isidora Jankov
,
Paul J. Schultz
,
Christopher J. Anderson
, and
Steven E. Koch

Abstract

The most significant precipitation events in California occur during the winter and are often related to synoptic-scale storms from the Pacific Ocean. Because of the terrain characteristics and the fact that the urban and infrastructural expansion is concentrated in lower elevation areas of the California Central Valley, a high risk of flooding is usually associated with these events. In the present study, the area of interest was the American River basin (ARB). The main focus of the present study was to investigate methods for Quantitative Precipitation Forecast (QPF) improvement by estimating the impact that various microphysical schemes, planetary boundary layer (PBL) schemes, and initialization methods have on cold season precipitation, primarily orographically induced. For this purpose, 3-km grid spacing Weather Research and Forecasting (WRF) model simulations of four Hydrometeorological Test bed (HMT) events were used. For each event, four different microphysical schemes and two different PBL schemes were used. All runs were initialized with both a diabatic Local Analysis and Prediction System (LAPS) “hot” start and 40-km eta analyses.

To quantify the impact of physical schemes, their interactions, and initial conditions upon simulated rain volume, the factor separation methodology was used. The results showed that simulated rain volume was particularly affected by changes in microphysical schemes for both initializations. When the initialization was changed from the LAPS to the eta analysis, the change in the PBL scheme and corresponding synergistic terms (which corresponded to the interactions between different microphysical and PBL schemes) resulted in a statistically significant impact on rain volume. In addition, by combining model runs based on the knowledge about their impact on simulated rain volume obtained through the factor separation methodology, the bias in simulated rain volume was reduced.

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Huiling Yuan
,
John A. McGinley
,
Paul J. Schultz
,
Christopher J. Anderson
, and
Chungu Lu

Abstract

High-resolution (3 km) time-lagged (initialized every 3 h) multimodel ensembles were produced in support of the Hydrometeorological Testbed (HMT)-West-2006 campaign in northern California, covering the American River basin (ARB). Multiple mesoscale models were used, including the Weather Research and Forecasting (WRF) model, Regional Atmospheric Modeling System (RAMS), and fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5). Short-range (6 h) quantitative precipitation forecasts (QPFs) and probabilistic QPFs (PQPFs) were compared to the 4-km NCEP stage IV precipitation analyses for archived intensive operation periods (IOPs). The two sets of ensemble runs (operational and rerun forecasts) were examined to evaluate the quality of high-resolution QPFs produced by time-lagged multimodel ensembles and to investigate the impacts of ensemble configurations on forecast skill. Uncertainties in precipitation forecasts were associated with different models, model physics, and initial and boundary conditions. The diabatic initialization by the Local Analysis and Prediction System (LAPS) helped precipitation forecasts, while the selection of microphysics was critical in ensemble design. Probability biases in the ensemble products were addressed by calibrating PQPFs. Using artificial neural network (ANN) and linear regression (LR) methods, the bias correction of PQPFs and a cross-validation procedure were applied to three operational IOPs and four rerun IOPs. Both the ANN and LR methods effectively improved PQPFs, especially for lower thresholds. The LR method outperformed the ANN method in bias correction, in particular for a smaller training data size. More training data (e.g., one-season forecasts) are desirable to test the robustness of both calibration methods.

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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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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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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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Ayumi Fujisaki-Manome
,
Lindsay E. Fitzpatrick
,
Andrew D. Gronewold
,
Eric J. Anderson
,
Brent M. Lofgren
,
Christopher Spence
,
Jiquan Chen
,
Changliang Shao
,
David M. Wright
, and
Chuliang Xiao

Abstract

Proper modeling of the turbulent heat fluxes over lakes is critical for accurate predictions of lake-effect snowfall (LES). However, model evaluation of such a process has not been possible because of the lack of direct flux measurements over lakes. The authors conducted the first-ever comparison of the turbulent latent and sensible heat fluxes between state-of-the-art numerical models and direct flux measurements over Lake Erie, focusing on a record LES event in southwest New York in November 2014. The model suite consisted of numerical models that were operationally and experimentally used to provide nowcasts and forecasts of weather and lake conditions. The models captured the rise of the observed turbulent heat fluxes, while the peak values varied significantly. This variation resulted in an increased spread of simulated lake temperature and cumulative evaporation as the representation of the model uncertainty. The water budget analysis of the atmospheric model results showed that the majority of the moisture during this event came from lake evaporation rather than a larger synoptic system. The unstructured-grid Finite-Volume Community Ocean Model (FVCOM) simulations, especially those using the Coupled Ocean–Atmosphere Response Experiment (COARE)-Met Flux algorithm, presented better agreement with the observed fluxes likely due to the model’s capability in representing the detailed spatial patterns of the turbulent heat fluxes and the COARE algorithm’s more realistic treatment of the surface boundary layer than those in the other models.

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