Object-based Verification of Atmospheric River Predictions in the Northeast Pacific

2021 ◽  
Author(s):  
Laurel L. DeHaan ◽  
Andrew C. Martin ◽  
Rachel R. Weihs ◽  
Luca Delle Monache ◽  
F. Martin Ralph
Keyword(s):  
Weather Prediction ◽  
Quantitative Measure ◽  
Cold Season ◽  
Forecast Skill ◽  
Atmospheric Rivers ◽  
Landslide Hazards ◽  
Object Based ◽  
Evaluation Mode ◽  
First Time ◽  
Effective Water

AbstractAccurate forecasts of atmospheric rivers (ARs) provide advance warning of flood and landslide hazards, as well as greatly aid effective water management. It is therefore critical to evaluate the skill of AR forecasts in numerical weather prediction (NWP) models. A new verification framework is proposed leveraging freely available software and metrics previously used for different applications. Specifically, AR detection and statistics are computed for the first time using the Method for Object-based Diagnostic Evaluation (MODE). In addition, the measure of effectiveness (MoE) is introduced as a new metric for understanding AR forecast skill in terms of size and location. The MoE provides a quantitative measure of the position of an entire forecasted AR compared to observation, regardless of whether the AR is making landfall. In addition, the MoE can provide qualitative information about the evolution of a forecast by lead time with implications about the predictability of an AR. We analyze AR forecast verification and skill using 11 years of cold season forecasts from two NWP models, one global and one regional. Four different thresholds of integrated vapor transport (IVT) are used in the verification revealing differences in forecast skill based on the strength of an AR. In addition to MoE, AR forecast skill is also addressed in terms of intensity error, landfall position error, and contingency table metrics.

Observations of Gulf of Tehuantepec Gap Wind Events from QuikSCAT: An Updated Event Climatology and Operational Model Evaluation

2010 ◽  
Vol 25 (2) ◽  
pp. 646-658 ◽  
Author(s):  
Michael J. Brennan ◽  
Hugh D. Cobb ◽  
Richard D. Knabb
Keyword(s):  
Weather Prediction ◽  
Surface Flux ◽  
Cold Season ◽  
Numerical Weather Prediction Model ◽  
Probability Of Detection ◽  
Gulf Of Tehuantepec ◽  
Wind Events ◽  
Seawinds Scatterometer ◽  
First Time ◽  
Vector Wind

Abstract A climatology of gale- and storm-force gap wind events in the Gulf of Tehuantepec is constructed for the first time using 10 yr of ocean surface vector wind data from the SeaWinds scatterometer on board NASA’s Quick Scatterometer (QuikSCAT) satellite. These wind events are among the most severe that occur within the National Hurricane Center’s (NHC) area of marine forecasting responsibility outside of tropical cyclones. The 10-yr climatology indicates that on average 11.9 gale-force events and 6.4 storm-force events occur in the Gulf of Tehuantepec each cold season. About 84% of these events occur between November and March, with the largest number of gale-force events occurring in December. Storm-force events are most frequent in January. Operational numerical weather prediction model forecasts of these events from the NCEP Global Forecast System (GFS) and North American Mesoscale (NAM) models were evaluated during the 2006/07 cold season. Results show that neither model is able to consistently forecast storm-force Tehuantepec wind events; however, the models do have some ability to forecast gale-force events. The NAM model showed a significant increase in probability of detection over the GFS, possibly due to increased horizontal and vertical resolutions as well as differences in boundary layer mixing and surface flux schemes. Finally, the prospects of observing these gap wind events in the post-QuikSCAT era will be discussed.

Printing ultrathin graphene oxide nanofiltration membranes for water purification

2017 ◽  
Vol 5 (39) ◽  
pp. 20860-20866 ◽  
Author(s):  
Mahdi Fathizadeh ◽  
Huynh Ngoc Tien ◽  
Konstantin Khivantsev ◽  
Jung-Tsai Chen ◽  
Miao Yu
Keyword(s):  
Graphene Oxide ◽  
Water Purification ◽  
Inkjet Printing ◽  
Low Cost ◽  
Nanofiltration Membranes ◽  
Highly Effective ◽  
First Time ◽  
Polymeric Supports ◽  
Effective Water

We demonstrated for the first time that inkjet printing can be a low-cost, easy, fast, and scalable method for depositing ultrathin (7.5–60 nm) uniform graphene oxide (GO) nanofiltration membranes on polymeric supports for highly effective water purification.

Proactive Quality Control: Observing System Simulation Experiments with the Lorenz ’96 Model

2019 ◽  
Vol 147 (1) ◽  
pp. 53-67 ◽  
Author(s):  
Tse-Chun Chen ◽  
Eugenia Kalnay
Keyword(s):  
Quality Control ◽  
Weather Prediction ◽  
Computational Cost ◽  
Network Size ◽  
Simulation Experiments ◽  
Systematic Testing ◽  
Kalman Gain ◽  
Lorenz 96 ◽  
First Time ◽  
Shed Light

Proactive quality control (PQC) is a fully flow-dependent QC for observations based on the ensemble forecast sensitivity to observations technique (EFSO). It aims at reducing the forecast skill dropout events suffered in operational numerical weather prediction by rejecting observations identified as detrimental by EFSO. Past studies show that individual dropout cases from the Global Forecast System (GFS) were significantly improved by noncycling PQC. In this paper, we perform for the first time cycling PQC experiments in a controlled environment with the Lorenz model to provide a systematic testing of the new method and possibly shed light on the optimal configuration of operational implementation. We compare several configurations and PQC update methods. It is found that PQC improvement is insensitive to the suboptimal configurations in DA, including ensemble size, observing network size, model error, and the length of DA window, but the improvements increase with the flaws in observations. More importantly, we show that PQC improves the analysis and forecast even in the absence of flawed observations. The study reveals that reusing the exact same Kalman gain matrix for PQC update not only provides the best result but requires the lowest computational cost among all the tested methods.

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.

scholarly journals The Effect of Upstream Convection on Downstream Precipitation

2007 ◽  
Vol 22 (2) ◽  
pp. 255-277 ◽  
Author(s):  
Kelly M. Mahoney ◽  
Gary M. Lackmann
Keyword(s):  
Moisture Transport ◽  
Weather Prediction ◽  
The United States ◽  
Cold Season ◽  
Precipitation Forecast ◽  
Study Region ◽  
Low Level ◽  
Convective Systems ◽  
Eta Model ◽  
Nwp Model

Abstract Operational forecasters in the southeast and mid-Atlantic regions of the United States have noted a positive quantitative precipitation forecast (QPF) bias in numerical weather prediction (NWP) model forecasts downstream of some organized, cold-season convective systems. Examination of cold-season cases in which model QPF guidance exhibited large errors allowed identification of two representative cases for detailed analysis. The goals of the case study analyses are to (i) identify physical mechanisms through which the upstream convection (UC) alters downstream precipitation amounts, (ii) determine why operational models are challenged to provide accurate guidance in these situations, and (iii) suggest future research directions that would improve model forecasts in these situations and allow forecasters to better anticipate such events. Two primary scenarios are identified during which downstream precipitation is altered in the presence of UC for the study region: (i) a fast-moving convective (FC) scenario in which organized convective systems oriented parallel to the lower-tropospheric flow are progressive relative to the parent synoptic system, and appear to disrupt poleward moisture transport, and (ii) a situation characterized by slower-moving convection (SC) relative to the parent system. Analysis of a representative FC case indicated that moisture consumption, stabilization via convective overturning, and modification of the low-level flow to a more westerly direction in the postconvective environment all appear to contribute to the reduction of downstream precipitation. In the FC case, operational Eta Model forecasts moved the organized UC too slowly, resulting in an overestimate of downstream moisture transport. A 4-km explicit convection model forecast from the Weather Research and Forecasting model produced a faster-moving upstream convective system and improved downstream QPF. In contrast to the FC event, latent heat release in the primary rainband is shown to enhance the low-level jet ahead of the convection in the SC case, thereby increasing moisture transport into the downstream region. A negative model QPF bias was observed in Eta Model forecasts for the SC event. Suggestions are made for precipitation forecasting in UC situations, and implications for NWP model configuration are discussed.

scholarly journals Background Error in WRF Model

2020 ◽  
Vol 16 ◽  
Keyword(s):  
Data Assimilation ◽  
Physical Properties ◽  
Covariance Matrix ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Wrf Model ◽  
Background Error ◽  
Numerical Weather ◽  
Error Covariance ◽  
First Time

WRF model have been tuned and tested over Georgia’s territory for years. First time in Georgia theprocess of data assimilation in Numerical weather prediction is developing. This work presents how forecasterror statistics appear in the data assimilation problem through the background error covariance matrix – B, wherethe variances and correlations associated with model forecasts are estimated. Results of modeling of backgrounderror covariance matrix for control variables using WRF model over Georgia with desired domain configurationare discussed and presented. The modeling was implemented in two different 3DVAR systems (WRFDA andGSI) and results were checked by pseudo observation benchmark cases using also default global and regional BEmatrixes. The mathematical and physical properties of the covariances are also reviewed.

scholarly journals Distinguishing the Cold Conveyor Belt and Sting Jet Airstreams in an Intense Extratropical Cyclone

2014 ◽  
Vol 142 (8) ◽  
pp. 2571-2595 ◽  
Author(s):  
Oscar Martínez-Alvarado ◽  
Laura H. Baker ◽  
Suzanne L. Gray ◽  
John Methven ◽  
Robert S. Plant
Keyword(s):  
Weather Prediction ◽  
Conveyor Belt ◽  
Numerical Weather Prediction Model ◽  
Strong Wind ◽  
Air Masses ◽  
Mesoscale Structures ◽  
Mesoscale Structure ◽  
Chemical Tracer ◽  
Strong Winds ◽  
First Time

Abstract Strong winds equatorward and rearward of a cyclone core have often been associated with two phenomena: the cold conveyor belt (CCB) jet and sting jets. Here, detailed observations of the mesoscale structure in this region of an intense cyclone are analyzed. The in situ and dropsonde observations were obtained during two research flights through the cyclone during the Diabatic Influences on Mesoscale Structures in Extratropical Storms (DIAMET) field campaign. A numerical weather prediction model is used to link the strong wind regions with three types of “airstreams” or coherent ensembles of trajectories: two types are identified with the CCB, hooking around the cyclone center, while the third is identified with a sting jet, descending from the cloud head to the west of the cyclone. Chemical tracer observations show for the first time that the CCB and sting jet airstreams are distinct air masses even when the associated low-level wind maxima are not spatially distinct. In the model, the CCB experiences slow latent heating through weak-resolved ascent and convection, while the sting jet experiences weak cooling associated with microphysics during its subsaturated descent. Diagnosis of mesoscale instabilities in the model shows that the CCB passes through largely stable regions, while the sting jet spends relatively long periods in locations characterized by conditional symmetric instability (CSI). The relation of CSI to the observed mesoscale structure of the bent-back front and its possible role in the cloud banding is discussed.

Forecasting excessive rainfall with random forests and a deterministic convection-allowing model

2021 ◽  
Author(s):  
Aaron J. Hill ◽  
Russ S. Schumacher
Keyword(s):  
Great Plains ◽  
Flash Flood ◽  
Weather Prediction ◽  
Stage Iv ◽  
Careful Consideration ◽  
Forecast Skill ◽  
Northern Great Plains ◽  
Training Models ◽  
Early Fall ◽  
Recurrence Intervals

AbstractApproximately seven years of daily initializations from the convection-allowing National Severe Storms Laboratory Weather Research and Forecasting model are used as inputs to train random forest (RF) machine learning models to probabilistically predict instances of excessive rainfall. Unlike other hazards, excessive rainfall does not have an accepted definition, so multiple definitions of excessive rainfall and flash flooding – including flash flood reports and 24-hr average recurrence intervals (ARIs) – are used to explore RF configuration forecast sensitivities. RF forecasts are analogous to operational Weather Prediction Center (WPC) day-1 Excessive Rainfall Outlooks (EROs) and their resolution, reliability, and skill are strongly influenced by rainfall definitions and how inputs are assembled for training. Models trained with 1-y ARI exceedances defined by the Stage-IV (ST4) precipitation analysis perform poorly in the northern Great Plains and southwest U.S., in part due to a high bias in the number of training events in these regions. Increasing the ARI threshold to 2 years or removing ST4 data from training, optimizing forecast skill geographically, and spatially averaging meteorological inputs for training generally results in improved CONUS-wide RF forecast skill. Both EROs and RF forecasts have seasonal skill – poor forecasts in the late fall and winter and skillful forecasts in the summer and early fall. However, the EROs are consistently and significantly better than their RF counterparts, regardless of RF configuration, particularly in the summer months. The results suggest careful consideration should be made when developing ML-based probabilistic precipitation forecasts with convection-allowing model inputs, and further development is necessary to consider these forecast products for operational implementation.

scholarly journals The effect of atmospheric rivers on cold-season heavy precipitation events in Iran

2020 ◽  
Author(s):  
Neda Esfandiari ◽  
Hassan Lashkari
Keyword(s):  
Extreme Events ◽  
Heavy Precipitation ◽  
Cold Season ◽  
Western Slope ◽  
Arid Areas ◽  
Gulf Of Aden ◽  
Zagros Mountains ◽  
Atmospheric Rivers ◽  
Semi Arid ◽  
Precipitation Events

Abstract Atmospheric rivers (ARs) as massive and concentrated water vapour paths can have a critical impact on extreme events in arid and semi-arid areas. This study investigated the effect of ARs on heavy precipitation events during the cold, rainy months (November–April) in Iran for 11 years. The results showed that 107 ARs had an influence on heavy precipitation, which providing partial moisture for Iran's precipitation. On average, 11 heavy precipitation days were linked to the presence of ARs in the six cold months of each year. During the study period, ARs accounted for almost 20–50% of the country's total heavy precipitation monthly. Although most ARs entered the country from the south through coastal areas, the western part of Iran, especially elevated stations along the western slope of the Zagros Mountains, received the highest heavy precipitation. Accordingly, about 66% of ARs directly originated from the Red Sea and the Gulf of Aden. Moreover, December experienced the highest frequency of ARs linked to heavy precipitation during the statistical period.

scholarly journals Large-Scale Flow Patterns Associated with Extreme Precipitation and Atmospheric Rivers over Norway

2019 ◽  
Vol 147 (4) ◽  
pp. 1415-1428 ◽  
Author(s):  
Imme Benedict ◽  
Karianne Ødemark ◽  
Thomas Nipen ◽  
Richard Moore
Keyword(s):  
Extreme Precipitation ◽  
Large Scale ◽  
Moisture Flux ◽  
Cold Season ◽  
Fuzzy Cluster ◽  
The South ◽  
Atmospheric Rivers ◽  
The North ◽  
Extreme Precipitation Events ◽  
Precipitation Events

Abstract A climatology of extreme cold season precipitation events in Norway from 1979 to 2014 is presented, based on the 99th percentile of the 24-h accumulated precipitation. Three regions, termed north, west, and south are identified, each exhibiting a unique seasonal distribution. There is a proclivity for events to occur during the positive phase of the NAO. The result is statistically significant at the 95th percentile for the north and west regions. An overarching hypothesis of this work is that anomalous moisture flux, or so-called atmospheric rivers (ARs), are integral to extreme precipitation events during the Norwegian cold season. An objective analysis of the integrated vapor transport illustrates that more than 85% of the events are associated with ARs. An empirical orthogonal function and fuzzy cluster technique is used to identify the large-scale weather patterns conducive to the moisture flux and extreme precipitation. Five days before the event and for each of the three regions, two patterns are found. The first represents an intense, southward-shifted jet with a southwest–northeast orientation. The second identifies a weak, northward-shifted, zonal jet. As the event approaches, regional differences become more apparent. The distinctive flow pattern conducive to orographically enhanced precipitation emerges in the two clusters for each region. For the north and west regions, this entails primarily zonal flow impinging upon the south–north-orientated topography, the difference being the latitude of the strong flow. In contrast, the south region exhibits a significant southerly component to the flow.

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