scholarly journals Potential Predictability during a Madden–Julian Oscillation Event

2017 ◽  
Vol 30 (14) ◽  
pp. 5345-5360 ◽  
Author(s):  
Charles Jones ◽  
Jimy Dudhia
Keyword(s):  
Initial Conditions ◽  
Spatial Scales ◽  
Forecast Error ◽  
Winter Season ◽  
Wrf Model ◽  
Forecast Errors ◽  
Madden Julian Oscillation ◽  
Potential Predictability ◽  
The Tropics ◽  
Mean Square Errors

The Madden–Julian oscillation (MJO) is an important source of predictability. The boreal 2004/05 winter is used as a case study to conduct predictability experiments with the Weather Research and Forecasting (WRF) Model. That winter season was characterized by an MJO event, weak El Niño, strong North Atlantic Oscillation, and extremely wet conditions over the contiguous United States (CONUS). The issues investigated are as follows: 1) growth of forecast errors in the tropics relative to the extratropics, 2) propagation of forecast errors from the tropics to the extratropics, 3) forecast error growth on spatial scales associated with MJO and non-MJO variability, and 4) the relative importance of MJO and non-MJO tropical variability on predictability of precipitation over CONUS. Root-mean-square errors in forecasts of normalized eddy kinetic energy (NEKE) (200 hPa) show that errors in initial conditions in the tropics grow faster than in the extratropics. Potential predictability extends out to about 4 days in the tropics and 9 days in the extratropics. Forecast errors in the tropics quickly propagate to the extratropics, as demonstrated by experiments in which initial conditions are only perturbed in the tropics. Forecast errors in NEKE (200 hPa) on scales related to the MJO grow slower than in non-MJO variability over localized areas in the tropics and short lead times. Potential predictability of precipitation extends to 1–5 days over most of CONUS but to longer leads (7–12 days) over regions with orographic precipitation in California. Errors in initial conditions on small scales relative to the MJO quickly grow, propagate to the extratropics, and degrade forecast skill of precipitation.

Initial Condition Sensitivity of Western Pacific Extratropical Transitions Determined Using Ensemble-Based Sensitivity Analysis

2009 ◽  
Vol 137 (10) ◽  
pp. 3388-3406 ◽  
Author(s):  
Ryan D. Torn ◽  
Gregory J. Hakim
Keyword(s):  
Sensitivity Analysis ◽  
Tropical Cyclone ◽  
Initial Conditions ◽  
Forecast Error ◽  
Wrf Model ◽  
Weather Research And Forecasting ◽  
Forecast Errors ◽  
Initial Condition ◽  
Ensemble Forecasts ◽  
The Relationship

Abstract An ensemble Kalman filter based on the Weather Research and Forecasting (WRF) model is used to generate ensemble analyses and forecasts for the extratropical transition (ET) events associated with Typhoons Tokage (2004) and Nabi (2005). Ensemble sensitivity analysis is then used to evaluate the relationship between forecast errors and initial condition errors at the onset of transition, and to objectively determine the observations having the largest impact on forecasts of these storms. Observations from rawinsondes, surface stations, aircraft, cloud winds, and cyclone best-track position are assimilated every 6 h for a period before, during, and after transition. Ensemble forecasts initialized at the onset of transition exhibit skill similar to the operational Global Forecast System (GFS) forecast and to a WRF forecast initialized from the GFS analysis. WRF ensemble forecasts of Tokage (Nabi) are characterized by relatively large (small) ensemble variance and greater (smaller) sensitivity to the initial conditions. In both cases, the 48-h forecast of cyclone minimum SLP and the RMS forecast error in SLP are most sensitive to the tropical cyclone position and to midlatitude troughs that interact with the tropical cyclone during ET. Diagnostic perturbations added to the initial conditions based on ensemble sensitivity reduce the error in the storm minimum SLP forecast by 50%. Observation impact calculations indicate that assimilating approximately 40 observations in regions of greatest initial condition sensitivity produces a large, statistically significant impact on the 48-h cyclone minimum SLP forecast. For the Tokage forecast, assimilating the single highest impact observation, an upper-tropospheric zonal wind observation from a Mongolian rawinsonde, yields 48-h forecast perturbations in excess of 10 hPa and 60 m in SLP and 500-hPa height, respectively.

scholarly journals ROAD TEMPERATURE MODELLING WITHOUT IN-SITU SENSORS

2017 ◽  
Vol 12 (4) ◽  
pp. 241-247 ◽  
Author(s):  
Karol Opara ◽  
Jan Zieliński
Keyword(s):  
Energy Balance ◽  
Urban Areas ◽  
Initial Conditions ◽  
Forecast Error ◽  
Weather Data ◽  
Forecast Errors ◽  
The Road ◽  
In Situ Sensors ◽  
Pavement Temperature

Modelling of the pavement temperature facilitates winter road maintenance. It is used for predicting the glaze formation and for scheduling the spraying of the de-icing brine. The road weather is commonly forecasted by solving the energy balance equations. It requires setting the initial vertical profile of the pavement temperature, which is often obtained from the Road Weather Information Stations. The paper proposes the use of average air temperature from seven preceding days as a pseudo-observation of the subsurface temperature. Next, the road weather model is run with a few days offset. It first uses the recent, historical weather data and then the available forecasts. This approach exploits the fact that the energy balance models tend to “forget” their initial conditions and converge to the baseline solution. The experimental verification was conducted using the Model of the Environment and Temperature of Roads and the data from a road weather station in Warsaw over a period of two years. The additional forecast error introduced by the proposed pseudo-observational initialization averages 1.2 °C in the first prediction hour and then decreases in time. The paper also discusses the use of Digital Surface Models to take into account the shading effects, which are an essential source of forecast errors in urban areas. Limiting the use of in-situ sensors opens a perspective for an economical, largescale implementation of road meteorological models.

Examination of Errors in Near-Surface Temperature and Wind from WRF Numerical Simulations in Regions of Complex Terrain

2013 ◽  
Vol 28 (3) ◽  
pp. 893-914 ◽  
Author(s):  
Hailing Zhang ◽  
Zhaoxia Pu ◽  
Xuebo Zhang
Keyword(s):  
Complex Terrain ◽  
Forecast Error ◽  
Wrf Model ◽  
Atmospheric Temperature ◽  
Lower Atmosphere ◽  
Forecast Errors ◽  
Atmospheric Conditions ◽  
Near Surface ◽  
System A ◽  
Synoptic Forcing

Abstract The performance of an advanced research version of the Weather Research and Forecasting Model (WRF) in predicting near-surface atmospheric temperature and wind conditions under various terrain and weather regimes is examined. Verification of 2-m temperature and 10-m wind speed and direction against surface Mesonet observations is conducted. Three individual events under strong synoptic forcings (i.e., a frontal system, a low-level jet, and a persistent inversion) are first evaluated. It is found that the WRF model is able to reproduce these weather phenomena reasonably well. Forecasts of near-surface variables in flat terrain generally agree well with observations, but errors also occur, depending on the predictability of the lower-atmospheric boundary layer. In complex terrain, forecasts not only suffer from the model's inability to reproduce accurate atmospheric conditions in the lower atmosphere but also struggle with representative issues due to mismatches between the model and the actual terrain. In addition, surface forecasts at finer resolutions do not always outperform those at coarser resolutions. Increasing the vertical resolution may not help predict the near-surface variables, although it does improve the forecasts of the structure of mesoscale weather phenomena. A statistical analysis is also performed for 120 forecasts during a 1-month period to further investigate forecast error characteristics in complex terrain. Results illustrate that forecast errors in near-surface variables depend strongly on the diurnal variation in surface conditions, especially when synoptic forcing is weak. Under strong synoptic forcing, the diurnal patterns in the errors break down, while the flow-dependent errors are clearly shown.

Intercomparison of an Ensemble Kalman Filter with Three- and Four-Dimensional Variational Data Assimilation Methods in a Limited-Area Model over the Month of June 2003

2011 ◽  
Vol 139 (2) ◽  
pp. 566-572 ◽  
Author(s):  
Meng Zhang ◽  
Fuqing Zhang ◽  
Xiang-Yu Huang ◽  
Xin Zhang
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Forecast Error ◽  
Wrf Model ◽  
Warm Season ◽  
Variational Data Assimilation ◽  
Lead Times ◽  
Limited Area ◽  
Forecast Errors

Abstract This study compares the performance of an ensemble Kalman filter (EnKF) with both the three-dimensional and four-dimensional variational data assimilation (3DVar and 4DVar) methods of the Weather Research and Forecasting (WRF) model over the contiguous United States in a warm-season month (June) of 2003. The data assimilated every 6 h include conventional sounding and surface observations as well as data from wind profilers, ships and aircraft, and the cloud-tracked winds from satellites. The performances of these methods are evaluated through verifying the 12- to 72-h forecasts initialized twice daily from the analysis of each method against the standard sounding observations. It is found that 4DVar has consistently smaller error than that of 3DVar for winds and temperature at all forecast lead times except at 60 and 72 h when their forecast errors become comparable in amplitude, while the two schemes have similar performance in moisture at all lead times. The forecast error of the EnKF is comparable to that of the 4DVar at 12–36-h lead times, both of which are substantially smaller than that of the 3DVar, despite the fact that 3DVar fits the sounding observations much more closely at the analysis time. The advantage of the EnKF becomes even more evident at 48–72-h lead times; the 72-h forecast error of the EnKF is comparable in magnitude to the 48-h error of 3DVar/4DVar.

scholarly journals Comparing Adjoint- and Ensemble-Sensitivity Analysis with Applications to Observation Targeting

2007 ◽  
Vol 135 (12) ◽  
pp. 4117-4134 ◽  
Author(s):  
Brian Ancell ◽  
Gregory J. Hakim
Keyword(s):  
Variance Reduction ◽  
Initial Conditions ◽  
Sampling Error ◽  
Forecast Error ◽  
Single Point ◽  
Error Variance ◽  
Initial Time ◽  
Forecast Errors ◽  
Adjoint Sensitivity ◽  
Time Optimal

Abstract The sensitivity of numerical weather forecasts to small changes in initial conditions is estimated using ensemble samples of analysis and forecast errors. Ensemble sensitivity is defined here by linear regression of analysis errors onto a given forecast metric. It is shown that ensemble sensitivity is proportional to the projection of the analysis-error covariance onto the adjoint-sensitivity field. Furthermore, the ensemble-sensitivity approach proposed here involves a small calculation that is easy to implement. Ensemble- and adjoint-based sensitivity fields are compared for a representative wintertime flow pattern near the west coast of North America for a 90-member ensemble of independent initial conditions derived from an ensemble Kalman filter. The forecast metric is taken for simplicity to be the 24-h forecast of sea level pressure at a single point in western Washington State. Results show that adjoint and ensemble sensitivities are very different in terms of location, scale, and magnitude. Adjoint-sensitivity fields reveal mesoscale lower-tropospheric structures that tilt strongly upshear, whereas ensemble-sensitivity fields emphasize synoptic-scale features that tilt modestly throughout the troposphere and are associated with significant weather features at the initial time. Optimal locations for targeting can easily be determined from ensemble sensitivity, and results indicate that the primary targeting locations are located away from regions of greatest adjoint and ensemble sensitivity. It is shown that this method of targeting is similar to previous ensemble-based methods that estimate forecast-error variance reduction, but easily allows for the application of statistical confidence measures to deal with sampling error.

Initialization and Potential Predictability of Soil Moisture in the Canadian Seasonal to Interannual Prediction System

2018 ◽  
Vol 31 (13) ◽  
pp. 5205-5224 ◽  
Author(s):  
Reinel Sospedra-Alfonso ◽  
William J. Merryfield
Keyword(s):  
Soil Moisture ◽  
Climate Models ◽  
Initial Conditions ◽  
Soil Depth ◽  
Global Climate ◽  
Global Climate Models ◽  
Prediction System ◽  
Potential Predictability ◽  
Temporal Variance ◽  
The Tropics

The initialization and potential predictability of soil moisture in CanCM4 hindcasts during 1981–2010 is assessed. CanCM4 is one of the two global climate models employed by the Canadian Seasonal to Interannual Prediction System (CanSIPS) providing operational multiseasonal forecasts for Environment and Climate Change Canada (ECCC). Soil moisture forecast initialization in CanSIPS is determined by the response of the land component to forcing from data-constrained model atmospheric fields. We evaluate hindcast initial conditions for soil moisture and its atmospheric forcings against observation-based datasets. Although model values of soil moisture variability compare relatively well with a blend of two reanalysis products, there is significant disagreement in the tropics and arid regions linked to biases in precipitation, as well as in snow-covered regions, likely the result of biases in the timing of snow onset and melt. The temporal variance of initial soil moisture anomalies is typically larger in regions of considerable precipitation variability and in cold continental areas of shallow soil depth. Appreciable variance of initial conditions, combined with persistence of the initial anomalies and the model’s ability to represent future climate variations, lead to potentially predictable soil moisture variance exceeding 60% of the total variance for up to 3–4 months in the tropics and 6–7 months in the mid- to high latitudes during hemispheric winter. Potential predictability at longer leads is primarily found in the tropics and extratropical areas of ENSO-teleconnected influences. We use lagged partial correlations to show that ENSO-teleconnected precipitation in CanCM4 is a likely source of potential predictability of soil moisture up to 1-yr lead in CanSIPS hindcasts.

The 3–4-Week MJO Prediction Skill in a GFDL Coupled Model

2015 ◽  
Vol 28 (13) ◽  
pp. 5351-5364 ◽  
Author(s):  
Baoqiang Xiang ◽  
Ming Zhao ◽  
Xianan Jiang ◽  
Shian-Jiann Lin ◽  
Tim Li ◽  
...  
Keyword(s):  
Initial Conditions ◽  
Strong Dependence ◽  
Intraseasonal Variability ◽  
Coupled Model ◽  
Prediction Skill ◽  
Geophysical Fluid Dynamics Laboratory ◽  
Madden Julian Oscillation ◽  
Geophysical Fluid Dynamics ◽  
Potential Predictability ◽  
Anomaly Correlation Coefficient

Abstract Based on a new version of the Geophysical Fluid Dynamics Laboratory (GFDL) coupled model, the Madden–Julian oscillation (MJO) prediction skill in boreal wintertime (November–April) is evaluated by analyzing 11 years (2003–13) of hindcast experiments. The initial conditions are obtained by applying a simple nudging technique toward observations. Using the real-time multivariate MJO (RMM) index as a predictand, it is demonstrated that the MJO prediction skill can reach out to 27 days before the anomaly correlation coefficient (ACC) decreases to 0.5. The MJO forecast skill also shows relatively larger contrasts between target strong and weak cases (32 versus 7 days) than between initially strong and weak cases (29 versus 24 days). Meanwhile, a strong dependence on target phases is found, as opposed to relative skill independence from different initial phases. The MJO prediction skill is also shown to be about 29 days during the Dynamics of the MJO/Cooperative Indian Ocean Experiment on Intraseasonal Variability in Year 2011 (DYNAMO/CINDY) field campaign period. This model’s potential predictability, the upper bound of prediction skill, extends out to 42 days, revealing a considerable unutilized predictability and a great potential for improving current MJO prediction.

scholarly journals Ensemble Mean Storm-Scale Performance in the Presence of Nonlinearity

2015 ◽  
Vol 143 (12) ◽  
pp. 5115-5133 ◽  
Author(s):  
Michael A. Hollan ◽  
Brian C. Ancell
Keyword(s):  
Prediction Models ◽  
Initial Conditions ◽  
Weather Prediction ◽  
Wrf Model ◽  
Forecast Errors ◽  
Ensemble Mean ◽  
Deterministic Solution ◽  
Significant Divergence ◽  
The Mean ◽  
And Control

Abstract The use of ensembles in numerical weather prediction models is becoming an increasingly effective method of forecasting. Many studies have shown that using the mean of an ensemble as a deterministic solution produces the most accurate forecasts. However, the mean will eventually lose its usefulness as a deterministic forecast in the presence of nonlinearity. At synoptic scales, this appears to occur between 12- and 24-h forecast time, and on storm scales it may occur significantly faster due to stronger nonlinearity. When this does occur, the question then becomes the following: Should the mean still be adhered to, or would a different approach produce better results? This paper will investigate the usefulness of the mean within a WRF Model utilizing an ensemble Kalman filter for severe convective events. To determine when the mean becomes unrealistic, the divergence of the mean of the ensemble (“mean”) and a deterministic forecast initialized from a set of mean initial conditions (“control”) are examined. It is found that significant divergence between the mean and control emerges no later than 6 h into a convective event. The mean and control are each compared to observations, with the control being more accurate for nearly all forecasts studied. For the case where the mean provides a better forecast than the control, an approach is offered to identify the member or group of members that is closest to the mean. Such a forecast will contain similar forecast errors as the mean, but unlike the mean, will be on an actual forecast trajectory.

scholarly journals Growth of Forecast Errors from Covariances Modeled by 4DVAR and ETKF Methods

2011 ◽  
Vol 139 (5) ◽  
pp. 1505-1518 ◽  
Author(s):  
Chiara Piccolo
Keyword(s):  
Weather Forecasting ◽  
Initial Conditions ◽  
Forecast Error ◽  
Forecast Errors ◽  
Model Errors ◽  
Background Error ◽  
Background Error Covariance ◽  
Error Covariance ◽  
Numerical Weather Forecasting ◽  
Ensemble Transform Kalman Filter

Numerical weather forecasting errors grow with time. Error growth results from the amplification of small perturbations due to atmospheric instability or from model deficiencies during model integration. In current NWP systems, the dimension of the forecast error covariance matrices is far too large for these matrices to be represented explicitly. They must be approximated. This paper focuses on comparing the growth of forecast error from covariances modeled by the Met Office operational four-dimensional variational data assimilation (4DVAR) and ensemble transform Kalman filter (ETKF) methods over a period of 24 h. The growth of forecast errors implied by 4DVAR is estimated by drawing a random sample of initial conditions from a Gaussian distribution with the standard deviations given by the background error covariance matrix and then evolving the sample forward in time using linearized dynamics. The growth of the forecast error modeled by the ETKF is estimated by propagating the full nonlinear model in time starting from initial conditions generated by an ETKF. This method includes model errors in two ways: by using an inflation factor and by adding model perturbations through a stochastic physics scheme. Finally, these results are compared with a benchmark of the climatological error. The forecast error predicted by the implicit evolution of 4DVAR does not grow, regardless of the dataset used to generate the static background error covariance statistics. The forecast error predicted by the ETKF grows more rapidly because the ETKF selects balanced initial perturbations, which project onto rapidly growing modes. Finally, in both cases it is not possible to disentangle the contribution of the initial condition error from the model error.

scholarly journals Improving High-Resolution Weather Forecasts Using the Weather Research and Forecasting (WRF) Model with an Updated Kain–Fritsch Scheme

2016 ◽  
Vol 144 (3) ◽  
pp. 833-860 ◽  
Author(s):  
Yue Zheng ◽  
Kiran Alapaty ◽  
Jerold A. Herwehe ◽  
Anthony D. Del Genio ◽  
Dev Niyogi
Keyword(s):  
High Resolution ◽  
Initial Conditions ◽  
Spatial Scales ◽  
Wrf Model ◽  
Cloud Microphysics ◽  
Cumulus Parameterization ◽  
Subgrid Scale ◽  
Grid Spacing ◽  
Dynamic Adjustment ◽  
Surface Precipitation

Abstract Efforts to improve the prediction accuracy of high-resolution (1–10 km) surface precipitation distribution and variability are of vital importance to local aspects of air pollution, wet deposition, and regional climate. However, precipitation biases and errors can occur at these spatial scales due to uncertainties in initial meteorological conditions and/or grid-scale cloud microphysics schemes. In particular, it is still unclear to what extent a subgrid-scale convection scheme could be modified to bring in scale awareness for improving high-resolution short-term precipitation forecasts in the WRF Model. To address these issues, the authors introduced scale-aware parameterized cloud dynamics for high-resolution forecasts by making several changes to the Kain–Fritsch (KF) convective parameterization scheme in the WRF Model. These changes include subgrid-scale cloud–radiation interactions, a dynamic adjustment time scale, impacts of cloud updraft mass fluxes on grid-scale vertical velocity, and lifting condensation level–based entrainment methodology that includes scale dependency. A series of 48-h retrospective forecasts using a combination of three treatments of convection (KF, updated KF, and the use of no cumulus parameterization), two cloud microphysics schemes, and two types of initial condition datasets were performed over the U.S. southern Great Plains on 9- and 3-km grid spacings during the summers of 2002 and 2010. Results indicate that 1) the source of initial conditions plays a key role in high-resolution precipitation forecasting, and 2) the authors’ updated KF scheme greatly alleviates the excessive precipitation at 9-km grid spacing and improves results at 3-km grid spacing as well. Overall, the study found that the updated KF scheme incorporated into a high-resolution model does provide better forecasts for precipitation location and intensity.

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