scholarly journals Skill of Rain–Snow Level Forecasts for Landfalling Atmospheric Rivers: A Multimodel Assessment Using California’s Network of Vertically Profiling Radars

2020 ◽  
Vol 21 (4) ◽  
pp. 751-771 ◽  
Author(s):  
Brian Henn ◽  
Rachel Weihs ◽  
Andrew C. Martin ◽  
F. Martin Ralph ◽  
Tashiana Osborne
Keyword(s):  
Sierra Nevada ◽  
Lead Time ◽  
Flood Hazard ◽  
Critical Factor ◽  
Forecast Model ◽  
Lead Times ◽  
Grid Spacing ◽  
Atmospheric Rivers ◽  
Operational Forecasts ◽  
Resources Planning

AbstractThe partitioning of rain and snow during atmospheric river (AR) storms is a critical factor in flood forecasting, water resources planning, and reservoir operations. Forecasts of atmospheric rain–snow levels from December 2016 to March 2017, a period of active AR landfalls, are evaluated using 19 profiling radars in California. Three forecast model products are assessed: a global forecast model downscaled to 3-km grid spacing, 4-km river forecast center operational forecasts, and 50-km global ensemble reforecasts. Model forecasts of the rain–snow level are compared with observations of rain–snow melting-level brightband heights. Models produce mean bias magnitudes of less than 200 m across a range of forecast lead times. Error magnitudes increase with lead time and are similar between models, averaging 342 m for lead times of 24 h or less and growing to 700–800 m for lead times of greater than 144 h. Observed extremes in the rain–snow level are underestimated, particularly for warmer events, and the magnitude of errors increases with rain–snow level. Storms with high rain–snow levels are correlated with larger observed precipitation rates in Sierra Nevada watersheds. Flood risk increases with rain–snow levels, not only because a greater fraction of the watershed receives rain, but also because warmer storms carry greater water vapor and thus can produce heavier precipitation. The uncertainty of flood forecasts grows nonlinearly with the rain–snow level for these reasons as well. High rain–snow level ARs are a major flood hazard in California and are projected to be more prevalent with climate warming.

Object-Based Evaluation of the Impact of Horizontal Grid Spacing on Convection-Allowing Forecasts

2013 ◽  
Vol 141 (10) ◽  
pp. 3413-3425 ◽  
Author(s):  
Aaron Johnson ◽  
Xuguang Wang ◽  
Fanyou Kong ◽  
Ming Xue
Keyword(s):  
Orientation Angle ◽  
Forecast Model ◽  
Weather Research And Forecasting ◽  
Lead Times ◽  
Eastern United States ◽  
Grid Spacing ◽  
Subjective Evaluations ◽  
Object Based ◽  
Object Area ◽  
The Impact

Abstract Forecasts generated by the Center for Analysis and Prediction of Storms with 1- and 4-km grid spacing using the Advanced Research Weather Research and Forecasting Model (ARW-WRF; ARW1 and ARW4, respectively) for the 2009–11 NOAA Hazardous Weather Testbed Spring Experiments are compared and verified. Object-based measures, including average values of object attributes, the object-based threat score (OTS), and the median of maximum interest (MMI) are used for the verification. Verification was first performed against observations at scales resolvable by each forecast model and then performed at scales resolvable by both models by remapping ARW1 to the ARW4 grid (ARW1to4). Thirty-hour forecasts of 1-h accumulated precipitation initialized at 0000 UTC on 22, 36, and 33 days during the spring of 2009, 2010, and 2011, respectively, are evaluated over a domain covering most of the central and eastern United States. ARW1, ARW1to4, and ARW4 all significantly overforecasted the number of objects during diurnal convection maxima. The overforecasts by ARW1 and ARW1to4 were more pronounced than ARW4 during the first convection maximum at 1-h lead time. The average object area and aspect ratio were closer to observations for ARW1 and ARW1to4 than for ARW4. None of the models showed a significant advantage over the others for average orientation angle and centroid location. Increased accuracy for ARW1, compared to ARW4, was statistically significant for the MMI but not the OTS. However, ARW1to4 had similar MMI and OTS as ARW4 at most lead times. These results are consistent with subjective evaluations that the greatest impact of grid spacing is on the smallest resolvable objects.

scholarly journals Development of GMDH-Based Storm Surge Forecast Models for Sakaiminato, Tottori, Japan

2020 ◽  
Vol 8 (10) ◽  
pp. 797
Author(s):  
Sooyoul Kim ◽  
Hajime Mase ◽  
Nguyen Ba Thuy ◽  
Masahide Takeda ◽  
Cao Truong Tran ◽  
...  
Keyword(s):  
Storm Surge ◽  
Lead Time ◽  
Forecast Model ◽  
Group Method ◽  
Lead Times ◽  
Input Layer ◽  
Input Parameters ◽  
Forecast Models ◽  
Essential Input ◽  
Hydrodynamic Data

The current study developed storm surge hindcast/forecast models with lead times of 5, 12, and 24 h at the Sakaiminato port, Tottori, Japan, using the group method of data handling (GMDH) algorithm. For training, local meteorological and hydrodynamic data observed in Sakaiminato during Typhoons Maemi (2003), Songda (2004), and Megi (2004) were collected at six stations. In the forecast experiments, the two typhoons, Maemi and Megi, as well as the typhoon Songda, were used for training and testing, respectively. It was found that the essential input parameters varied with the lead time of the forecasts, and many types of input parameters relevant to training were necessary for near–far forecasting time-series of storm surge levels. In addition, it was seen that the inclusion of the storm surge level at the input layer was critical to the accuracy of the forecast model.

scholarly journals A Spatiotemporal Convolutional Gated Recurrent Unit Network for Mean Wave Period Field Forecasting

2021 ◽  
Vol 9 (4) ◽  
pp. 383
Author(s):  
Ting Yu ◽  
Jichao Wang
Keyword(s):  
Lead Time ◽  
Wave Period ◽  
Data Driven ◽  
Lead Times ◽  
Proposed Model ◽  
Data Driven Approach ◽  
Mean Wave Period ◽  
Scattering Index ◽  
Gated Recurrent Unit ◽  
Methods Numerical

Mean wave period (MWP) is one of the key parameters affecting the design of marine facilities. Currently, there are two main methods, numerical and data-driven methods, for forecasting wave parameters, of which the latter are widely used. However, few studies have focused on MWP forecasting, and even fewer have investigated it with spatial and temporal information. In this study, correlations between ocean dynamic parameters are explored to obtain appropriate input features, significant wave height (SWH) and MWP. Subsequently, a data-driven approach, the convolution gated recurrent unit (Conv-GRU) model with spatiotemporal characteristics, is utilized to field forecast MWP with 1, 3, 6, 12, and 24-h lead times in the South China Sea. Six points at different locations and six consecutive moments at every 12-h intervals are selected to study the forecasting ability of the proposed model. The Conv-GRU model has a better performance than the single gated recurrent unit (GRU) model in terms of root mean square error (RMSE), the scattering index (SI), Bias, and the Pearson’s correlation coefficient (R). With the lead time increasing, the forecast effect shows a decreasing trend, specifically, the experiment displays a relatively smooth forecast curve and presents a great advantage in the short-term forecast of the MWP field in the Conv-GRU model, where the RMSE is 0.121 m for 1-h lead time.

scholarly journals A Preliminary Look at the Social Perspective of Warn-on-Forecast: Preferred Tornado Warning Lead Time and the General Public’s Perceptions of Weather Risks

2011 ◽  
Vol 3 (2) ◽  
pp. 128-140 ◽  
Author(s):  
S. Hoekstra ◽  
K. Klockow ◽  
R. Riley ◽  
J. Brotzge ◽  
H. Brooks ◽  
...  
Keyword(s):  
Lead Time ◽  
Stated Preference ◽  
The United States ◽  
Public Understanding ◽  
Lead Times ◽  
Present System ◽  
The Public ◽  
Tornado Warnings ◽  
Tornado Warning ◽  
Weather Risks

Abstract Tornado warnings are currently issued an average of 13 min in advance of a tornado and are based on a warn-on-detection paradigm. However, computer model improvements may allow for a new warning paradigm, warn-on-forecast, to be established in the future. This would mean that tornado warnings could be issued one to two hours in advance, prior to storm initiation. In anticipation of the technological innovation, this study inquires whether the warn-on-forecast paradigm for tornado warnings may be preferred by the public (i.e., individuals and households). The authors sample is drawn from visitors to the National Weather Center in Norman, Oklahoma. During the summer and fall of 2009, surveys were distributed to 320 participants to assess their understanding and perception of weather risks and preferred tornado warning lead time. Responses were analyzed according to several different parameters including age, region of residency, educational level, number of children, and prior tornado experience. A majority of the respondents answered many of the weather risk questions correctly. They seemed to be familiar with tornado seasons; however, they were unaware of the relative number of fatalities caused by tornadoes and several additional weather phenomena each year in the United States. The preferred lead time was 34.3 min according to average survey responses. This suggests that while the general public may currently prefer a longer average lead time than the present system offers, the preference does not extend to the 1–2-h time frame theoretically offered by the warn-on-forecast system. When asked what they would do if given a 1-h lead time, respondents reported that taking shelter was a lesser priority than when given a 15-min lead time, and fleeing the area became a slightly more popular alternative. A majority of respondents also reported the situation would feel less life threatening if given a 1-h lead time. These results suggest that how the public responds to longer lead times may be complex and situationally dependent, and further study must be conducted to ascertain the users for whom the longer lead times would carry the most value. These results form the basis of an informative stated-preference approach to predicting public response to long (>1 h) warning lead times, using public understanding of the risks posed by severe weather events to contextualize lead-time demand.

scholarly journals Tornado Warnings, Lead Times, and Tornado Casualties: An Empirical Investigation

2008 ◽  
Vol 23 (2) ◽  
pp. 246-258 ◽  
Author(s):  
Kevin M. Simmons ◽  
Daniel Sutter
Keyword(s):  
United States ◽  
Lead Time ◽  
Empirical Investigation ◽  
Conventional Wisdom ◽  
Lead Times ◽  
Time Intervals ◽  
Consistent Effect ◽  
Tornado Warnings ◽  
The Relationship

Abstract Conventional wisdom holds that improved tornado warnings will reduce tornado casualties, because longer lead times on warnings provide extra opportunities to alert residents who can then take precautions. The relationship between warnings and casualties is examined using a dataset of tornadoes in the contiguous United States between 1986 and 2002. Two questions are examined: Does a warning issued on a tornado reduce the resulting number of fatalities and injuries? Do longer lead times reduce casualties? It is found that warnings have had a significant and consistent effect on tornado injuries, with a reduction of over 40% at some lead time intervals. The results for fatalities are mixed. An increase in lead time up to about 15 min reduces fatalities, while lead times longer than 15 min increase fatalities compared with no warning. The fatality results beyond 15 min, however, depend on five killer tornadoes and consequently are not robust.

HYDROLOGICAL DROUGHT FORECASTS USING MARKOV CHAINS AND REGRESSION MODELS (A CASE STUDY FOR NORTHWEST OF ALGERIA)

2021 ◽  
pp. 90-103
Author(s):  
A. RAHMOUNI ◽  
◽  
M. MEDDI ◽  
A. HAMOUDI SAAED ◽  
◽  
...  
Keyword(s):  
Lead Time ◽  
Robust Regression ◽  
Forecast Accuracy ◽  
Hydrological Drought ◽  
Lead Times ◽  
Chain Process ◽  
Markov Switching Model ◽  
North West ◽  
Switching Model ◽  
The North

An effective drought forecast is an important measure to mitigate some of its most damaging impacts. In this study we compare the effectiveness of two models: Markov Switching Model (MSM) and Robust Regression Model (RRM) with three different approaches to forecast hydrological drought events in the north-west of Algeria using Standardized Runoff Index (SRI). The validation of these models is carried out by hydro-climatic series of 41 stations for the period of 1968-2009. The values of SRI 3, SRI 6, and SRI 12 have been forecasted over lead times of 1 and 6 months. The performance of forecast results is measured using R2 and RMSE. For the lead time of 1 month, the results are quite similar for both models with slight superiority for the Markov chain process. The addition of the SPI or RDI indices as independent variables improves this performance for some stations while it decreases accuracy for other stations. However, forecast accuracy declines significantly as the lead time increases to 6 months particularly for regression results.

Changes in mechanisms and intensity of Western U.S. floods, 1960-2013

2021 ◽  
Author(s):  
Jessica Fayne ◽  
Huilin Huang ◽  
Mike Fischella ◽  
Yufei Liu ◽  
Zhaoxin Ban ◽  
...  
Keyword(s):  
Extreme Precipitation ◽  
Large Scale ◽  
Critical Factor ◽  
Flood Frequency ◽  
Atmospheric Rivers ◽  
Convective Storms ◽  
Noticeable Increase ◽  
Complex Interactions ◽  
Different Types ◽  
The Impact

<p>Extreme precipitation, a critical factor in flooding, has selectively increased with warmer temperatures in the Western U.S. Despite this, the streamflow measurements have captured no noticeable increase in large-scale flood frequency or intensity. As flood studies have mostly focused on specific flood events in particular areas, analyses of large-scale floods and their changes have been scarce. For floods during 1960-2013, we identify six flood generating mechanisms (FGMs) that are prominent across the Western U.S., including atmospheric rivers and non-atmospheric rivers, monsoons, convective storms, radiation-driven snowmelt, and rain-on-snow, in order to identify to what extent different types of floods are changing based on the dominant FGM. The inconsistency between extreme precipitation and lack of flood increase suggests that the impact of climate change on flood risk has been modulated by hydro-meteorological and physiographic processes such as sharp increases in temperature that drive increased evapotranspiration and decreased soil moisture. Our results emphasize the importance of FGMs in understanding the complex interactions of flooding and climatic changes and explain the broad spatiotemporal changes that have occurred across the vast Western U.S. for the past 50 years.</p>

scholarly journals Assessment of Numerical Weather Prediction Model Reforecasts of the Occurrence, Intensity, and Location of Atmospheric Rivers along the West Coast of North America

2018 ◽  
Vol 146 (10) ◽  
pp. 3343-3362 ◽  
Author(s):  
Kyle M. Nardi ◽  
Elizabeth A. Barnes ◽  
F. Martin Ralph
Keyword(s):  
Water Vapor ◽  
North America ◽  
Weather Prediction ◽  
Vapor Transport ◽  
Water Vapor Transport ◽  
Lead Times ◽  
Model Errors ◽  
The West ◽  
Atmospheric Rivers ◽  
Landfall Location

AbstractAtmospheric rivers (ARs)—narrow corridors of high atmospheric water vapor transport—occur globally and are associated with flooding and maintenance of the water supply. Therefore, it is important to improve forecasts of AR occurrence and characteristics. Although prior work has examined the skill of numerical weather prediction (NWP) models in forecasting atmospheric rivers, these studies only cover several years of reforecasts from a handful of models. Here, we expand this previous work and assess the performance of 10–30 years of wintertime (November–February) AR landfall reforecasts from the control runs of nine operational weather models, obtained from the International Subseasonal to Seasonal (S2S) Project database. Model errors along the west coast of North America at leads of 1–14 days are examined in terms of AR occurrence, intensity, and landfall location. Occurrence-based skill approaches that of climatology at 14 days, while models are, on average, more skillful at shorter leads in California, Oregon, and Washington compared to British Columbia and Alaska. We also find that the average magnitude of landfall integrated water vapor transport (IVT) error stays fairly constant across lead times, although overprediction of IVT is common at later lead times. Finally, we show that northward landfall location errors are favored in California, Oregon, and Washington, although southward errors occur more often than expected from climatology. These results highlight the need for model improvements, while helping to identify factors that cause model errors.

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