Predictability of storm wave heights in the ice-free Beaufort Sea

A predictability study on wave forecast of the Arctic Ocean is necessary to help identify hazardous areas and ensure sustainable shipping along the trans-Arctic routes. To assist with validation of the Arctic Ocean wave model, two drifting wave buoys were deployed off Point Barrow, Alaska for two months in September 2016. Both buoys measured significant wave heights exceeding 4 m during two different storm events on 19 September and 22 October. The NOAA-WAVEWATCH III^? model with 16-km resolution was forced using wind and sea ice reanalysis data and obtained general agreement with the observation. The September storm was reproduced well; however, model accuracy deteriorated in October with a negative wave height bias of around 1 m during the October storm. Utilising reanalysis data, including the most up-to-date ERA5, this study investigated the cause: grid resolution, wind and ice forcing, and in situ sea level pressure observations assimilated for reanalysis. The analysis has found that there is a 20% reduction of in situ SLP observations in the area of interest, presumably due to fewer ships and deployment options during the sea ice advance period. The 63-member atmospheric ensemble reanalysis, ALERA2, has shown that this led to a larger ensemble spread in the October monthly mean wind field compared to September. Since atmospheric physics is complex during sea ice advance, it is speculated that the elevated uncertainty of synoptic-scale wind caused the negative wave model bias. This has implications for wave hindcasts and forecasts in the Arctic Ocean.     

Quantitative assessment of short‐term rainfall forecasts from radar nowcasts and MM5 forecasts

Short‐term Quantitative Precipitation Forecasts (QPFs) can be achieved from numerical weather prediction (NWP) models or radar nowcasting, that is the extrapolation of the precipitation at a future time from consecutive radar scans. Hybrid forecasts obtained by merging rainfall forecasts from radar nowcasting and NWP models are potentially more skilful than either radar nowcasts or NWP rainfall forecasts alone. This paper provides an assessment of deterministic and probabilistic high‐resolution QPFs achieved by implementing the Short‐term Ensemble Prediction System developed by the UK Met Office. Both radar nowcasts and hybrid forecasts have been performed. The results show that the performance of both deterministic nowcasts and deterministic hybrid forecasts decreases with increasing rainfall intensity and spatial resolution. The results also show that the blending with the NWP forecasts improves the performance of the forecasting system. Probabilistic hybrid forecasts have been obtained through the modelling of a stochastic noise component to produce a number of equally likely ensemble members, and the comparative assessment of deterministic and probabilistic hybrid forecasts shows that the probabilistic forecasting system is characterised by a higher discrimination accuracy than the deterministic one. Copyright © 2011 John Wiley & Sons, Ltd.     

Development and testing of a coupled ocean–atmosphere mesoscale ensemble prediction system

A coupled ocean–atmosphere mesoscale ensemble prediction system has been developed by the Naval Research Laboratory. This paper describes the components and implementation of the system and presents baseline results from coupled ensemble simulations for two tropical cyclones. The system is designed to take into account major sources of uncertainty in: (1) non-deterministic dynamics, (2) model error, and (3) initial states. The purpose of the system is to provide mesoscale ensemble forecasts for use in probabilistic products, such as reliability and frequency of occurrence, and in risk management applications. The system components include COAMPS® (Coupled Ocean/Atmosphere Mesoscale Prediction System) and NCOM (Navy Coastal Ocean Model) for atmosphere and ocean forecasting and NAVDAS (NRL Atmospheric Variational Data Assimilation System) and NCODA (Navy Coupled Ocean Data Assimilation) for atmosphere and ocean data assimilation. NAVDAS and NCODA are 3D-variational (3DVAR) analysis schemes. The ensembles are generated using separate applications of the Ensemble Transform (ET) technique in both the atmosphere (for moving or non-moving nests) and the ocean. The atmospheric ET is computed using wind, temperature, and moisture variables, while the oceanographic ET is derived from ocean current, temperature, and salinity variables. Estimates of analysis error covariance, which is used as a constraint in the ET, are provided by the ocean and atmosphere 3DVAR assimilation systems. The newly developed system has been successfully tested for a variety of configurations, including differing model resolution, number of members, forecast length, and moving and fixed nest options. Results from relatively coarse resolution (～27-km) ensemble simulations of Hurricanes Hanna and Ike demonstrate that the ensemble can provide valuable uncertainty information about the storm track and intensity, though the ensemble mean provides only a small amount of improved predictive skill compared to the deterministic control member.     

A One-dimensional Ensemble Forecast and Assimilation System for Fog Prediction

A probabilistic fog forecast system was designed based on two high resolution numerical 1-D models called COBEL and PAFOG. The 1-D models are coupled to several 3-D numerical weather prediction models and thus are able to consider the effects of advection. To deal with the large uncertainty inherent to fog forecasts, a whole ensemble of 1-D runs is computed using the two different numerical models and a set of different initial conditions in combination with distinct boundary conditions. Initial conditions are obtained from variational data assimilation, which optimally combines observations with a first guess taken from operational 3-D models. The design of the ensemble scheme computes members that should fairly well represent the uncertainty of the current meteorological regime. Verification for an entire fog season reveals the importance of advection in complex terrain. The skill of 1-D fog forecasts is significantly improved if advection is considered. Thus the probabilistic forecast system has the potential to support the forecaster and therefore to provide more accurate fog forecasts.     

An evaluation and improvement of tropical cyclone prediction in the western North Pacific basin from global ensemble forecasts

Forecasts of tropical cyclones(TCs) of the western North Pacific basin during the period of July to August 2018,especially of Rumbia(2018), Ampil(2018) and Jongdari(2018) that made landfall over Shanghai, have opposed great challenges for numerical models and forecasters. The predictive skill of these TCs are analyzed based on ensemble forecasts of ECMWF and NCEP. Results of the overall performance show that ensemble forecasts of ECMWF generally have higher predictive skill of track and intensity forecasts than those of NCEP. Specifically, ensemble forecasts of ECMWF have higher predictive skill of intensity forecasts for Rumbia(2018) and Ampil(2018) than those of NCEP, and both have low predictive skill of intensity forecasts for Jongdari(2018) at peak intensity. To improve the predictive skill of ensemble forecasts for TCs, a method that estimates adaptive weights for members of an ensemble forecast is proposed. The adaptive weights are estimated based on the fit of ensemble priors and posteriors to observations. The performances of ensemble forecasts of ECMWF and NCEP using the adaptive weights are generally improved for track and intensity forecasts. The advantages of the adaptive weights are more prominent for ensemble forecasts of ECMWF than for those of NCEP.     

High-resolution ensemble forecasting for the Gulf of Mexico eddies and fronts

High-resolution models and realistic boundary conditions are necessary to reproduce the mesoscale dynamics of the Gulf of Mexico (GOM). In order to achieve this, we use a nested configuration of the Hybrid Coordinate Ocean Model (HYCOM), where the Atlantic TOPAZ system provides lateral boundary conditions to a high-resolution (5 km) model of the GOM . However, such models cannot provide accurate forecasts of mesoscale variability, such as eddy shedding event, without data assimilation. Eddy shedding events involve the rapid growth of nonlinear instabilities that are difficult to forecast. The known sources of error are the initial state, the atmospheric condition, and the lateral boundary condition. We present here the benefit of using a small ensemble forecast (10 members) for providing confidence indices for the prediction, while using a data assimilation scheme based on optimal interpolation. Our set of initial states is provided by using different values of a data assimilation parameter, while the atmospheric and lateral boundary conditions are perturbed randomly. Changes in the data assimilation parameter appear to control the main position of the large features of the GOM in the initial state, whereas changes in the boundary conditions (lateral and atmospheric) appears to control the propagation of cyclonic eddies at their boundary. The ensemble forecast is tested for the shedding of Eddy Yankee (2006). The Loop Current and eddy fronts observed from ocean color and altimetry are almost always within the estimated positions from the ensemble forecast. The ensemble spread is correlated both in space and time to the forecast error, which implies that confidence indices can be provided in addition to the forecast. Finally, the ensemble forecast permits the optimization of a data assimilation parameter for best performance at a given forecast horizon.     

Prediction and predictability of a catastrophic local extreme precipitation event through cloud-resolving ensemble analysis and forecasting with Doppler radar observations

Local extreme rain usually resulted in disasters such as flash floods and landslides. Upon today, it is still one of the most difficult tasks for operational weather forecast centers to predict those events accurately. In this paper, we simulate an extreme precipitation event with ensemble Kalman filter (EnKF) assimilation of Doppler radial-velocity observations, and analyze the uncertainties of the assimilation. The results demonstrate that, without assimilation radar data, neither a single initialization of deterministic forecast nor an ensemble forecast with adding perturbations or multiple physical parameterizations can predict the location of strong precipitation. However, forecast was significantly improved with assimilation of radar data, especially the location of the precipitation. The direct cause of the improvement is the buildup of a deep mesoscale convection system with EnKF assimilation of radar data. Under a large scale background favorable for mesoscale convection, efficient perturbations of upstream mid-low level meridional wind and moisture are key factors for the assimilation and forecast. Uncertainty still exists for the forecast of this case due to its limited predictability. Both the difference of large scale initial fields and the difference of analysis obtained from EnKF assimilation due to small amplitude of initial perturbations could have critical influences to the event's prediction. Forecast could be improved through more cycles of EnKF assimilation. Sensitivity tests also support that more accurate forecasts are expected through improving numerical models and observations.     

Hydrologic evaluation of spatially disaggregated global ensemble rainfall forecasts

A statistic–stochastic multi‐fractal downscaling technique was evaluated from a hydrologic point of view. Ensemble hydrologic forecasts with a time step of 3 h were performed for original and disaggregated ensemble rainfall forecasts issued by the Canadian Global Ensemble Prediction System in its 2009 operational version. This hydro‐meteorological operational forecasting chain was conducted using the hydrological model SWMM5. The model was implemented on a small 6‐km² urban catchment located in the Québec City region. The hydrological evaluation was based on the comparison of forecasted flows to the observed ones, calculating several deterministic and probabilistic scores, and drawing rank histograms and reliability diagrams. Disaggregated products led to a better representation of the ensemble members' dispersion. This disaggregation technique represents an interesting way of bridging the gap between the meteorological models' resolution and the high degree of spatial precision sometimes required by hydrological models in their precipitation representation. Copyright © 2013 John Wiley & Sons, Ltd.     

Relative role of subinertial and superinertial modes in the coastal long wave response forced by the landfall of a tropical cyclone

A set of numerical experiments has been performed in order to analyze the long-wave response of the coastal ocean to a translating mesoscale atmospheric cyclone approaching the coastline at a normal angle. An idealized two-slope shelf topography is chosen. The model is forced by a radially symmetric atmospheric pressure perturbation with a corresponding gradient wind field. The cyclone's translation speed, radius, and the continental shelf width are considered as parameters whose impact on the long wave period, modal structure, and amplitude is studied. Subinertial continental shelf waves (CSW) dominate the response under typical forcing conditions and on the narrower shelves. They propagate in the downstream (in the sense of Kelvin wave propagation) direction. Superinertial edge wave modes have higher free surface amplitudes and faster phase speeds than the CSW modes. While potentially more dangerous, edge waves are not as common as subinertial shelf waves because their generation requires a wide, gently sloping shelf and a storm system translating at a relatively high (∼10 m s⁻¹ or faster) speed. A relatively smaller size of an atmospheric cyclone also favors edge wave generation. Edge waves with the highest amplitude (up to 60% of the forced storm surge) propagate upstream. They are produced by a storm system with an Eulerian time scale equal to the period of a zero-mode edge wave with the wavelength of the storm spatial scale. Large amplitude edge waves were generated during Hurricane Wilma's landfall (2005) on the West Florida shelf with particularly severe flooding occurring upstream of the landfall site.     

Application of data assimilation for improving forecast of water levels and residual currents in Singapore regional waters

Hydrodynamic models are commonly used for predicting water levels and currents in the deep ocean, ocean margins and shelf seas. Their accuracy is typically limited by factors, such as the complexity of the coastal geometry and bathymetry, plus the uncertainty in the flow forcing (deep ocean tide, winds and pressure). In Southeast Asian waters with its strongly hydrodynamic characteristics, the lack of detailed marine observations (bathymetry and tides) for model validation is an additional factor limiting flow representation. This paper deals with the application of ensemble Kalman filter (EnKF)-based data assimilation with the purpose of improving the deterministic model forecast. The efficacy of the EnKF is analysed via a twin experiment conducted with the 2D barotropic Singapore regional model. The results show that the applied data assimilation can improve the forecasts significantly in this complex flow regime.     

Impact of assimilating Taiwan’s coastal radar radial velocity on forecasting Typhoon Morakot (2009) in southeastern China using a WRF-based EnKF

This study explores for the first time the impact of assimilating radial velocity (Vr) observations from a single or multiple Taiwan’s coastal radars on tropical cyclone (TC) forecasting after landfall in the Chinese mainland by using a Weather Research and Forecasting model (WRF)-based ensemble Kalman filter (EnKF) data assimilation system. Typhoon Morakot (2009), which caused widespread damage in the southeastern coastal regions of the mainland after devastating Taiwan, was chosen as a case study. The results showed that assimilating Taiwan’s radar Vr data improved environmental field and steering flow and produced a more realistic TC position and structure in the final EnKF cycling analysis. Thus, the subsequent TC track and rainfall forecasts in southeastern China were improved. In addition, better observations of the TC inner core by Taiwan’s radar was a primary factor in improving TC rainfall forecast in the Chinese mainland.     

Forecast-skill-based simulation of streamflow forecasts

Streamflow forecasts are updated periodically in real time, thereby facilitating forecast evolution. This study proposes a forecast-skill-based model of forecast evolution that is able to simulate dynamically updated streamflow forecasts. The proposed model applies stochastic models that deal with streamflow variability to generate streamflow scenarios, which represent cases without forecast skill of future streamflow. The model then employs a coefficient of prediction to determine forecast skill and to quantify the streamflow variability ratio explained by the forecast. By updating the coefficients of prediction periodically, the model efficiently captures the evolution of streamflow forecast. Simulated forecast uncertainty increases with increasing lead time; and simulated uncertainty during a specific future period decreases over time. We combine the statistical model with an optimization model and design a hypothetical case study of reservoir operation. The results indicate the significance of forecast skill in forecast-based reservoir operation. Shortage index reduces as forecast skill increases and ensemble forecast outperforms deterministic forecast at a similar forecast skill level. Moreover, an effective forecast horizon exists beyond which more forecast information does not contribute to reservoir operation and higher forecast skill results in longer effective forecast horizon. The results illustrate that the statistical model is efficient in simulating forecast evolution and facilitates analysis of forecast-based decision making.     

Evaluation of monthly precipitation forecasting skill of the National Multi‐model Ensemble in the summer season

As a test bed, the National Multi‐model Ensemble (NMME) comprises seven climate models from different sources, including the National Oceanic and Atmospheric Administration, the National Aeronautics and Space Administration, the National Center for Atmospheric Research and the International Research Institute for Climate and Society. It provides 89 ensemble members of precipitation forecasts at different lead times. Precipitation forecasting from climate models has been applied to provide streamflow forecasts, and its utility in water resource system operation has been demonstrated in the literature. In this study, 1‐month‐ahead precipitation forecasts from NMME are evaluated for 945 grid points of 1°‐by‐1° resolution over the continental USA using mean square error and rank probability score. The temporal and spatial variabilities of the forecasting skill over different months of the summer season are discussed. The relation between forecasting uncertainty and observed precipitation is investigated. Such analyses have implications for monthly operational forecasts and water resource management at the watershed scale. Copyright © 2013 John Wiley & Sons, Ltd.     

Forecasting flash floods using data-based mechanistic models and NORA radar rainfall forecasts

Abstract

Data-based mechanistic (DBM) models can offer a parsimonious representation of catchment dynamics. They have been shown to provide reliable accurate flood forecasts in many hydrological situations. In this work, the DBM methodology is applied to forecast flash floods in a small Alpine catchment. Compared to previous DBM modelling studies, the catchment response is rapid. The use of novel radar-derived ensemble quantitative precipitation forecasts based on analogues to drive the DBM model allows the forecast horizon to be increased to a level useful for emergency response. The characterization of the predictive uncertainty in the resulting hydrological forecasts is discussed and a framework for its representation illustrated.

Editor Z.W. Kundzewicz; Guest editor R.J. Moore     

Planning for Air Quality Concerns of the Future

— In recognition of growing needs for forecasts of air quality and atmospheric deposition to accompany classical weather forecasts, a new generation of atmospheric prediction models is slowly evolving. These share the common feature that atmospheric chemistry will be directly incorporated into advanced forecast schemes. It is argued that in most practical applications, the over-riding need is not for accurate prediction of some quantifiable air quality component, but rather a forecast of the probability of harmful consequences to exposure. In this event, it is not concentrations that need to be forecast, but the probability that concentrations will exceed some predetermined level at which consequences could be harmful. This argument extends from emergency response applications to ecosystem decline.     

Short-term optimal operation of water systems using ensemble forecasts

Short-term water system operation can be realized using Model Predictive Control (MPC). MPC is a method for operational management of complex dynamic systems. Applied to open water systems, MPC provides integrated, optimal, and proactive management, when forecasts are available. Notwithstanding these properties, if forecast uncertainty is not properly taken into account, the system performance can critically deteriorate.Ensemble forecast is a way to represent short-term forecast uncertainty. An ensemble forecast is a set of possible future trajectories of a meteorological or hydrological system. The growing ensemble forecasts’ availability and accuracy raises the question on how to use them for operational management.The theoretical innovation presented here is the use of ensemble forecasts for optimal operation. Specifically, we introduce a tree based approach. We called the new method Tree-Based Model Predictive Control (TB-MPC). In TB-MPC, a tree is used to set up a Multistage Stochastic Programming, which finds a different optimal strategy for each branch and enhances the adaptivity to forecast uncertainty. Adaptivity reduces the sensitivity to wrong forecasts and improves the operational performance.TB-MPC is applied to the operational management of Salto Grande reservoir, located at the border between Argentina and Uruguay, and compared to other methods.     

An Application of the Multi-Physics Ensemble Kalman Filter to Typhoon Forecast

This study examines the roles of the multi-physics approach in accounting for model errors for typhoon forecasts with the local ensemble transform Kalman filter (LETKF). Experiments with forecasts of Typhoon Conson (2010) using the weather research and forecasting (WRF) model show that use of the WRF’s multiple physical parameterization schemes to represent the model uncertainties can help the LETKF provide better forecasts of Typhoon Conson in terms of the forecast errors, the ensemble spread, the root mean square errors, the cross-correlation between mass and wind field as well as the coherent structure of the ensemble spread along the storm center. Sensitivity experiments with the WRF model show that the optimum number of the multi-physics ensemble is roughly equal to the number of combinations of different physics schemes assigned in the multi-physics ensemble. Additional idealized experiments with the Lorenz 40-variable model to isolate the dual roles of the multi-physics ensemble in correcting model errors and expanding the local ensemble space show that the multi-physics approach appears to be more essential in augmenting the local rank representation of the LETKF algorithm rather than directly accounting for model errors during the early cycles. The results in this study suggest that the multi-physics approach is a good option for short-range forecast applications with full physics models in which the spinup of the ensemble Kalman filter may take too long for the ensemble spread to capture efficiently model errors and cross-correlations among model variables.     

The Representation of Tropospheric Water Vapor Over Low-Latitude Oceans in (Re-)analysis: Errors,Impacts, and the Ability to Exploit Current and Prospective Observations

This paper addresses the representation of lower tropospheric water vapor in the meteorological analyses—fully detailed estimates of atmospheric state—providing the wide temporal and spatial coverage used in many process studies. Analyses are produced in a cycle combining short forecasts from initial conditions with data assimilation that optimally estimates the state of the atmosphere from the previous forecasts and new observations, providing initial conditions for the next set of forecasts. Estimates of water vapor are among the less certain aspects of the state because the quantity poses special challenges for data assimilation while being particularly sensitive to the details of model parameterizations. Over remote tropical oceans observations of water vapor come from two sources: passive observations at microwave or infrared wavelengths that provide relatively strong constraints over large areas on column-integrated moisture but relatively coarse vertical resolution, and occultations of Global Positioning System provide much higher accuracy and vertical resolution but are relatively spatially coarse. Over low-latitude oceans, experiences with two systems suggest that current analyses reproduce much of the large-scale variability in integrated water vapor but have systematic errors in the representation of the boundary layer with compensating errors in the free troposphere; these errors introduce errors of order 10% in radiative heating rates through the free troposphere. New observations, such as might be obtained by future observing systems, improve the estimates of water vapor but this improvement is lost relatively quickly, suggesting that exploiting better observations will require targeted improvements to global forecast models.