Storm Forecasting Research Articles

Predicting the potential for lightning activity in Mediterranean storms based on the Weather Research and Forecasting (WRF) model dynamic and microphysical fields

A new parameter is introduced: the lightning potential index (LPI), which is a measure of the potential for charge generation and separation that leads to lightning flashes in convective thunderstorms. The LPI is calculated within the charge separation region of clouds between 0°C and −20°C, where the noninductive mechanism involving collisions of ice and graupel particles in the presence of supercooled water is most effective. As shown in several case studies using the Weather Research and Forecasting (WRF) model with explicit microphysics, the LPI is highly correlated with observed lightning. It is suggested that the LPI may be a useful parameter for predicting lightning as well as a tool for improving weather forecasting of convective storms and heavy rainfall.

Evaluating the impact of lightning data assimilation on mesoscale model simulations of a flash flood inducing storm

Flash floods are associated with highly localized convective storms producing heavy rainfall. Quantitative precipitation forecasting of such storms will potentially benefit from explicit representations of deep moist convection in numerical weather prediction models. However, explicit representation of moist convection is still not viable in operational mesoscale models, which rely on convective parameterizations for issuing short to medium-range forecasts. In this study we evaluate a technique that uses regional Cloud-to-Ground (CG) lightning observations to define areas of deep moist convection in thunderstorms and adjust the model-generated precipitation fields in those regions. The study focuses on a major flash flood inducing storm in central Europe (23 August 2005) that was simulated with the aid of an operational weather forecasting system (POSEIDON system based on Eta/NCEP model). The performance of the technique is assessed using as reference distributed rainfall estimates from a network of radar observations. The results indicate that CG lightning data can offer sufficient information to increase the mesoscale model skill in reproducing local convective precipitation that leads to flash floods. The model error correction is shown to be proportional to the density of lightning occurrence, making the technique potentially suitable for operational forecasting of flash flood inducing thunderstorms.

Additive Noise for Storm-Scale Ensemble Data Assimilation

Abstract An “additive noise” method for initializing ensemble forecasts of convective storms and maintaining ensemble spread during data assimilation is developed and tested for a simplified numerical cloud model (no radiation, terrain, or surface fluxes) and radar observations of the 8 May 2003 Oklahoma City supercell. Every 5 min during a 90-min data-assimilation window, local perturbations in the wind, temperature, and water-vapor fields are added to each ensemble member where the reflectivity observations indicate precipitation. These perturbations are random but have been smoothed so that they have correlation length scales of a few kilometers. An ensemble Kalman filter technique is used to assimilate Doppler velocity observations into the cloud model. The supercell and other nearby cells that develop in the model are qualitatively similar to those that were observed. Relative to previous storm-scale ensemble methods, the additive-noise technique reduces the number of spurious cells and their negative consequences during the data assimilation. The additive-noise method is designed to maintain ensemble spread within convective storms during long periods of data assimilation, and it adapts to changing storm configurations. It would be straightforward to use this method in a mesoscale model with explicit convection and inhomogeneous storm environments.

Recurving Tropical Cyclones: Singular Vector Sensitivity and Downstream Impacts

Abstract Singular vectors (SVs) are used to study the sensitivity of 2-day forecasts of recurving tropical cyclones (TCs) in the western North Pacific to changes in the initial state. The SVs are calculated using the tangent and adjoint models of the Navy Operational Global Atmospheric Prediction System (NOGAPS) for 72 forecasts for 18 TCs in the western North Pacific during 2006. In addition to the linear SV calculation, nonlinear perturbation experiments are also performed in order to examine 1) the similarity between nonlinear and linear perturbation growth and 2) the downstream impacts over the North Pacific and North America that result from changes to the 2-day TC forecast. Both nonrecurving and recurving 2-day storm forecasts are sensitive to changes in the initial state in the near-storm environment (in an annulus approximately 500 km from the storm center). During recurvature, sensitivity develops to the northwest of the storm, usually associated with a trough moving in from the west. These upstream sensitivities can occur as far as 4000 km to the northwest of the storm, over the Asian mainland, which has implications for adaptive observations. Nonlinear perturbation experiments indicate that the linear calculations reflect case-to-case variability in actual nonlinear perturbation growth fairly well, especially when the growth is large. The nonlinear perturbations show that for recurving tropical cyclones, small initial perturbations optimized to change the 2-day TC forecast can grow and propagate downstream quickly, reaching North America in 5 days. The fastest 5-day perturbation growth is associated with recurving storm forecasts that occur when the baroclinic instability over the North Pacific is relatively large. These results suggest that nonlinear forecasts perturbed using TC SVs may have utility for predicting the downstream impact of TC forecast errors over the North Pacific and North America.

Visualizing Multiple Measures of Forecast Quality

AbstractA method for visually representing multiple measures of dichotomous (yes–no) forecast quality (probability of detection, false alarm ratio, bias, and critical success index) in a single diagram is presented. Illustration of the method is provided using performance statistics from two previously published forecast verification studies (snowfall density and convective initiation) and a verification of several new forecast datasets: Storm Prediction Center forecasts of severe storms (nontornadic and tornadic), Hydrometeorological Prediction Center forecasts of heavy precipitation (greater than 12.5 mm in a 6-h period), National Weather Service Forecast Office terminal aviation forecasts (ceiling and visibility), and medium-range ensemble forecasts of 500-hPa height anomalies. The use of such verification metrics in concert with more detailed investigations to advance forecasting is briefly discussed.

Operational aerosol and dust storm forecasting

The U. S. Navy now conducts operational forecasting of aerosols and dust storms on global and regional scales. The Navy Aerosol Analysis and Prediction System (NAAPS) is run four times per day and produces 6-day forecasts of sulfate, smoke, dust and sea salt aerosol concentrations and visibility for the entire globe. The Coupled Ocean Atmosphere Mesoscale Prediction System (COAMPS®) is run twice daily for Southwest Asia and produces 3-day forecasts of dust, smoke, and visibility. The graphical output from these models is available on the Internet (www.nrlmry.navy.mil/aerosol/). The aerosol optical properties are calculated for each specie for each forecast output time and used for sea surface temperature (SST) retrieval corrections, regional electro-optical (EO) propagation assessments, and the development of satellite algorithms. NAAPS daily aerosol optical depth (AOD) values are compared with the Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) AOD values. Visibility forecasts are compared quantitatively with surface synoptic reports.

Potential predictors for spring season dust storm forecast in Inner Mongolia, China

This study identifies potential predictors for seasonal forecast of dust storm frequency for the Inner Mongolia region of China. Regionally averaged antecedent annual precipitation anomaly has a significant influence on spring dust storm frequency. It is strongly linked to the frequency of severe dust storms that produce strong impact on the downstream Beijing-Tianjin area. A hindcast that uses precipitation anomaly to predict dust storm frequency in the following spring significantly outperforms climatologic forecast. Limited soil moisture observations indicate that an abundant annual precipitation increases the soil moisture of subsurface layer in the subsequent spring, which in turn improves vegetation growth that could lead to a reduction in the frequency of dust storms.

The weather and climate of the tropics: Part 7 – Tropical revolving storms

In earlier parts of the series, there was much discussion of broad-scale convection in the near-equatorial humid tropics. The development of areas of deep cumuliform and layer clouds associated with low-pressure systems, however, is often a major cause of disruption due to flooding, landslip, wind damage and high seas. As a result, much effort is put into forecasting the development and motion of these tropical revolving storms. Such storms can be forecast evermore accurately, particularly up to about 48 hours in advance, not least by the UK Met Office (McCallum and Heming, 2006) and considerable effort is put into the improvement of these forecasts (Elsberry, 2006). The forecasts of tropical revolving storms are co-ordinated by a number of designated Regional Specialized Meteorological Centres (RSMCs) of the World Meteorological Organization (WMO) to produce a single official forecast to be used worldwide. These centres are at Honolulu (centralnorth Pacific), La Reunion (southern Indian Ocean), Miami (north Atlantic and northeast Pacific), Nadi (south-west Pacific), New Delhi (northern Indian Ocean) and Tokyo (north-west Pacific). Forecasts are also prepared at various forecasting centres in Australasia, as well as Pretoria and Mauritius, although these are not officially RSMCs, but regional Tropical Cyclone Warning Centres. These centres are shown in Figure 1. Most numerical weather prediction centres running global forecast models contribute to the global tropical-storm forecasting effort. The reliability of the Met Office global model has led to the development of a web-based seasonal storm forecast for the North Atlantic, based on the Hadley Centre GloSea seasonal climate model (http://www. metoffice.gov.uk/weather/tropicalcyclone/ northatlantic.html). Useful seasonal forecasts are also produced by the Benfield Hazard Research Centre of University College, London (http://forecast.mssl.ucl. ac.uk/shadow/tracker/dynamic/main.html).

A stochastic method for convective storm identification, tracking and nowcasting

The convective storm identification, tracking and nowcasting method is one of the important nowcasting methodologies against severe convective weather. In severe convective cases, such as storm shape or rapid velocity changes, existing methods are apt to provide unsatisfied storm identification, tracking and nowcasting results. To overcome these difficulties, this paper proposes a novel approach to identify, track and short-term forecast (nowcast) of convective storms. A mathematical morphology-based storm identification method is adopted which can identify storm cells accurately in a cluster of storms. As for the difficult tracking problem, sequential Monte Carlo (SMC) method is utilized to simplify the tracking process. It is not only inherently suitable for handling complicated splits and mergers, but also capable of handling the case of storm-missing detection. In order to provide more accurate forecast of a storm position, this method takes the advantages of the cross-correlation method. The qualitative and quantitative evaluations show the efficiency and robustness of the proposed approach.

Model bias correction for dust storm forecast using ensemble Kalman filter

First attempt to correct model bias in a dust transport model using ensemble Kalman filter (EnKF) assimilation targeting heavy dust episodes during the period of 15–24 March 2002 over north China is successfully performed. The uncertainty of dust emissions and surface wind fields are taken into account individually and simultaneously to correct their biases. The 24‐h surface forecasts are significantly improved with the root mean square error reduced by more than 45% on 20 March and by 50% on 21 March after correcting the biases. The results indicate that there are high biases due to the dust emissions and surface wind fields. These biases converge to the values similar with those obtained in previous sensitivity analyses indicating that the EnKF can accurately correct the bias. The corrected total dust emissions are decreased more than 33%. However, when considered simultaneously, they do not converge to the same results as those considered individually. This indicates that the two biases can compensate for each other in terms of predicted surface dust concentration.

Data assimilation of dust aerosol observations for the CUACE/dust forecasting system

Abstract. A data assimilation system (DAS) was developed for the Chinese Unified Atmospheric Chemistry Environment – Dust (CUACE/Dust) forecast system and applied in the operational forecasts of sand and dust storm (SDS) in spring 2006. The system is based on a three dimensional variational method (3D-Var) and uses extensively the measurements of surface visibility (phenomena) and dust loading retrieval from the Chinese geostationary satellite FY-2C. By a number of case studies, the DAS was found to provide corrections to both under- and over-estimates of SDS, presenting a major improvement to the forecasting capability of CUACE/Dust in the short-term variability in the spatial distribution and intensity of dust concentrations in both source regions and downwind areas. The seasonal mean Threat Score (TS) over the East Asia in spring 2006 increased from 0.22 to 0.31 by using the data assimilation system, a 41% enhancement. The forecast results with DAS usually agree with the dust loading retrieved from FY-2C and visibility distribution from surface meteorological stations, which indicates that the 3D-Var method is very powerful by the unification of observation and numerical model to improve the performance of forecast model.

An Ensemble Kalman Filter for severe dust storm data assimilation over China

Abstract. An Ensemble Kalman Filter (EnKF) data assimilation system was developed for a regional dust transport model. This paper applied the EnKF method to investigate modeling of severe dust storm episodes occurring in March 2002 over China based on surface observations of dust concentrations to explore the impact of the EnKF data assimilation systems on forecast improvement. A series of sensitivity experiments using our system demonstrates the ability of the advanced EnKF assimilation method using surface observed PM10 in North China to correct initial conditions, which leads to improved forecasts of dust storms. However, large errors in the forecast may arise from model errors (uncertainties in meteorological fields, dust emissions, dry deposition velocity, etc.). This result illustrates that the EnKF requires identification and correction model errors during the assimilation procedure in order to significantly improve forecasts. Results also show that the EnKF should use a large inflation parameter to obtain better model performance and forecast potential. Furthermore, the ensemble perturbations generated at the initial time should include enough ensemble spreads to represent the background error after several assimilation cycles.

Scale-Selective Verification of Rainfall Accumulations from High-Resolution Forecasts of Convective Events

Abstract The development of NWP models with grid spacing down to ∼1 km should produce more realistic forecasts of convective storms. However, greater realism does not necessarily mean more accurate precipitation forecasts. The rapid growth of errors on small scales in conjunction with preexisting errors on larger scales may limit the usefulness of such models. The purpose of this paper is to examine whether improved model resolution alone is able to produce more skillful precipitation forecasts on useful scales, and how the skill varies with spatial scale. A verification method will be described in which skill is determined from a comparison of rainfall forecasts with radar using fractional coverage over different sized areas. The Met Office Unified Model was run with grid spacings of 12, 4, and 1 km for 10 days in which convection occurred during the summers of 2003 and 2004. All forecasts were run from 12-km initial states for a clean comparison. The results show that the 1-km model was the most skillful over all but the smallest scales (approximately <10–15 km). A measure of acceptable skill was defined; this was attained by the 1-km model at scales around 40–70 km, some 10–20 km less than that of the 12-km model. The biggest improvement occurred for heavier, more localized rain, despite it being more difficult to predict. The 4-km model did not improve much on the 12-km model because of the difficulties of representing convection at that resolution, which was accentuated by the spinup from 12-km fields.

Using a neural network to make operational forecasts of ionospheric variations and storms at Kokubunji, Japan

Abstract An operational model was developed for forecasting ionospheric variations and storms at Kokubunji (35⊙N, 139⊙E), 24 hours in advance, by using a neural network. The ionospheric critical frequency (foF2) shows periodic variabilities from days to the solar cycle length and also shows sporadic changes known as ionospheric storms caused by geomagnetic storms (of solar disturbance origin). The neural network was trained for the target parameter of foF2 at each local time and input parameters of solar flux, sunspot number, day of the year, Kindex at Kakioka. The training was conducted using the data obtained for the period from 1960 to 1984. The method was validated for the period from 1985 to 2003. The trained network can be used for daily forecasting ionospheric variations including storms using prompt daily reports of K-index, sunspot number, and solar flux values available on-line.

Influence of GPS Precipitable Water Vapor Retrievals on Quantitative Precipitation Forecasting in Southern California

Abstract The effects of precipitable water vapor (PWV) retrievals from the Southern California Integrated GPS Network (SCIGN) on quantitative precipitation forecast (QPF) skill are examined over two flood-prone regions of Southern California: Santa Barbara (SB) and Ventura County (VC). Two sets of QPFs are made, one using the initial water vapor field from the NCEP 40-km Eta initial analysis, and another in which the initial Eta water vapor field is modified by incorporating the PWV data from the SCIGN receivers. Lateral boundary data for the QPFs, as well as the hydrostatic component of the GPS zenith delay data, are estimated from the Eta analysis. Case studies of a winter storm on 2 February during the 1997/98 El Niño, and storms leading up to the La Conchita, California, landslide on 10 January 2005, show notably improved QPFs for the first 3–6 h with the addition of GPS PWV data. For a total of 47 winter storm forecasts between February 1998 and January 2005 the average absolute QPF improvement is small; however, QPF improvements exceed 5 mm in several underpredicted rainfall events, with GPS data also improving most cases with overpredicted rainfall. The GPS improvements are most significant (above or near the 2σ level) when the low-level winds off the coast of Southern California are from the southern (SW to SE) quadrant. To extend the useful forecast skill enhancement beyond six hours, however, additional sources of water vapor data over broader areas of the adjacent Pacific Ocean are needed.

U.S. Space Environment Center renamed

The U.S. National Oceanic and Atmospheric Administration's Space Environment Center changed its name on 1 October to the NOAA Space Weather Prediction Center. The new name reflects the rapidly growing importance of solar storm forecasts to the nation's economy and security, explained the center's director, Thomas Bogdan. Adverse space weather can disrupt power grids, disturb communications and navigation systems, interrupt satellite signals, and increase radiation doses to astronauts as well as to airline crews and passengers. For more information, see http://www.swpc.noaa.gov/.

Impact of the Different Components of 4DVAR on the Global Forecast System of the Meteorological Service of Canada

Abstract A four-dimensional variational data assimilation (4DVAR) scheme has recently been implemented in the medium-range weather forecast system of the Meteorological Service of Canada (MSC). The new scheme is now composed of several additional and improved features as compared with the three-dimensional variational data assimilation (3DVAR): the first guess at the appropriate time from the full-resolution model trajectory is used to calculate the misfit to the observations; the tangent linear of the forecast model and its adjoint are employed to propagate the analysis increment and the gradient of the cost function over the 6-h assimilation window; a comprehensive set of simplified physical parameterizations is used during the final minimization process; and the number of frequently reported data, in particular satellite data, has substantially increased. The impact of these 4DVAR components on the forecast skill is reported in this article. This is achieved by comparing data assimilation configurations that range in complexity from the former 3DVAR with the implemented 4DVAR over a 1-month period. It is shown that the implementation of the tangent-linear model and its adjoint as well as the increased number of observations are the two features of the new 4DVAR that contribute the most to the forecast improvement. All the other components provide marginal though positive impact. 4DVAR does not improve the medium-range forecast of tropical storms in general and tends to amplify the existing, too early extratropical transition often observed in the MSC global forecast system with 3DVAR. It is shown that this recurrent problem is, however, more sensitive to the forecast model than the data assimilation scheme employed in this system. Finally, the impact of using a shorter cutoff time for the reception of observations, as the one used in the operational context for the 0000 and 1200 UTC forecasts, is more detrimental with 4DVAR. This result indicates that 4DVAR is more sensitive to observations at the end of the assimilation window than 3DVAR.

WILLIAM DOBERCK - DOUBLE STAR ASTRONOMER

We outline the role of astronomy in the career of William Doberck (1852-1941). After taking a PhD in astronomy at the University of Jena in 1873, he accepted a position as superintendent of Markree Observatory in the west of Ireland. There he refurbished the great 13-inch refractor and spent nine years observing mostly double star systems, paying only such attention to meteorological monitoring as was required of his position. In 1883 he became the founding Director of a new observatory in Hong Kong, a post which he held for 24 years. His frustrations in attempting to continue his purely astronomical work, not assuaged by his combative and prickly personality, and in the face of the strictly practical demands of that mercantile society for comprehensive storm forecasting, are described. Finally, his observations in retirement in England, and his overall contribution to astronomy, are summarised.

COAMPS Real-Time Dust Storm Forecasting during Operation Iraqi Freedom

Abstract Dust storms are a significant weather phenomenon in the Iraq region in winter and spring. Real-time dust forecasting using the U.S. Navy’s Coupled Ocean–Atmospheric Mesoscale Prediction System (COAMPS) with an in-line dust aerosol model was conducted for Operation Iraqi Freedom (OIF) in March and April 2003. Daily forecasts of dust mass concentration, visibility, and optical depth were produced out to 72 h on nested grids of 9-, 27-, and 81-km resolution in two-way nest interaction. In this paper, the model is described, as are examples of its application during OIF. The model performance is evaluated using ground weather reports, visibility observations, and enhanced satellite retrievals. The comparison of the model forecasts with observations for the severe dust storms of OIF shows that COAMPS predicted the arrival and retreat of the major dust events within 2 h. In most cases, COAMPS predicted the intensity (reduction in visibility) of storms with an error of less than 1 km. The forecasts of the spatial distribution of dust fronts and dust plumes were consistent with those seen in the satellite images and the corresponding cold front observations. A statistical analysis of dust-related visibility for the OIF period reveals that COAMPS generates higher bias, rms, and relative errors at the stations having high frequencies of dust storms and near the source areas. The calculation of forecast accuracy shows that COAMPS achieved a probability of dust detection of 50%–90% and a threat score of 0.3–0.55 at the stations with frequent dust storms. Overall, the model predicted more than 85% of the observed dust and nondust weather events at the stations used in the verification for the OIF period. Comparisons of the forecast rates and statistical errors for the forecasts of different lengths (12–72 h) for both dust and dynamics fields during the strong dust storm of 26 March revealed little dependence of model accuracy on forecast length, implying that the successive COAMPS forecasts were consistent for the severest OIF dust event.

内湾を対象としたリアルタイム波浪予測システムの開発

The Flood Fighting Law stipulates that prefectural governors shall issue a flood fighting warning of storm surges on coasts previously recognized as being at high risk of storm surges. However, forecasts of storm surges and high waves before a typhoon approaches are insufficient information to issue a flood fighting warning because the forecasts do not indicate that the wave runup exceeds coastal dikes and wave forecasts are imprecise. Also, wave forecasts are not very precise near coasts because wave models do not consider wave transformation in shallow water such as shoaling and refraction. Therefore we are developing a real-time wave forecast system for inland seas to ensure proper flood fighting warnings