Abstract

This study proposes a dynamical performance-ranking method (called the Du–Zhou ranking method) to predict the relative performance of individual ensemble members by assuming the ensemble mean is a good estimation of the truth. The results show that the method 1) generally works well, especially for shorter ranges such as a 1-day forecast; 2) has less error in predicting the extreme (best and worst) performers than the intermediate performers; 3) works better when the variation in performance among ensemble members is large; 4) works better when the model bias is small; 5) works better in a multimodel than in a single-model ensemble environment; and 6) works best when using the magnitude difference between a member and its ensemble mean as the “distance” measure in ranking members. The ensemble mean and median generally perform similarly to each other.

This method was applied to a weighted ensemble average to see if it can improve the ensemble mean forecast over a commonly used, simple equally weighted ensemble averaging method. The results indicate that the weighted ensemble mean forecast has a smaller systematic error. This superiority of the weighted over the simple mean is especially true for smaller-sized ensembles, such as 5 and 11 members, but it decreases with the increase in ensemble size and almost vanishes when the ensemble size increases to 21 members. There is, however, little impact on the random error and the spatial patterns of ensemble mean forecasts. These results imply that it might be difficult to improve the ensemble mean by just weighting members when an ensemble reaches a certain size. However, it is found that the weighted averaging can reduce the total forecast error more when a raw ensemble-mean forecast itself is less accurate. It is also expected that the effectiveness of weighted averaging should be improved when the ensemble spread is improved or when the ranking method itself is improved, although such an improvement should not be expected to be too big (probably less than 10%, on average).

Abstract

By using air–vegetation–soil layer coupled model equations as weak constraints, a variational method is developed to compute sensible and latent heat fluxes from conventional observations obtained at meteorological surface stations. This method also retrieves the top soil layer water content (daytime only) and the surface skin temperature as by-products. The method is applied to Oklahoma Mesonet data collected in the summer of 1995. Fluxes computed for selected Mesonet stations are verified against those obtained by the surface energy and radiation balance system at Atmospheric Radiation Measurement (ARM) Cloud and Radiation Testbed (CART) sites closest to the selected Mesonet stations. The retrieved values of soil water content are also compared with the direct measurements at the closest ARM CART stations. With data provided by the dense Mesonet, the method is shown to be useful in deriving the mesoscale distributions and temporal variabilities of surface fluxes, soil water content, and skin temperature. The method is unique in that it provides an additional means to derive flux fields directly from conventional surface observations.

Abstract

A new multivariable-based diagnostic fog-forecasting method has been developed at NCEP. The selection of these variables, their thresholds, and the influences on fog forecasting are discussed. With the inclusion of the algorithm in the model postprocessor, the fog forecast can now be provided centrally as direct NWP model guidance. The method can be easily adapted to other NWP models. Currently, knowledge of how well fog forecasts based on operational NWP models perform is lacking. To verify the new method and assess fog forecast skill, as well as to account for forecast uncertainty, this fog-forecasting algorithm is applied to a multimodel-based Mesoscale Ensemble Prediction System (MEPS). MEPS consists of 10 members using two regional models [the NCEP Nonhydrostatic Mesoscale Model (NMM) version of the Weather Research and Forecasting (WRF) model and the NCAR Advanced Research version of WRF (ARW)] with 15-km horizontal resolution. Each model has five members (one control and four perturbed members) using the breeding technique to perturb the initial conditions and was run once per day out to 36 h over eastern China for seven months (February–September 2008). Both deterministic and probabilistic forecasts were produced based on individual members, a one-model ensemble, and two-model ensembles. A case study and statistical verification, using both deterministic and probabilistic measuring scores, were performed against fog observations from 13 cities in eastern China. The verification was focused on the 12- and 36-h forecasts.

By applying the various approaches, including the new fog detection scheme, ensemble technique, multimodel approach, and the increase in ensemble size, the fog forecast accuracy was steadily and dramatically improved in each of the approaches: from basically no skill at all [equitable threat score (ETS) = 0.063] to a skill level equivalent to that of warm-season precipitation forecasts of the current NWP models (0.334). Specifically, 1) the multivariable-based fog diagnostic method has a much higher detection capability than the liquid water content (LWC)-only based approach. Reasons why the multivariable approach works better than the LWC-only method were also illustrated. 2) The ensemble-based forecasts are, in general, superior to a single control forecast measured both deterministically and probabilistically. The case study also demonstrates that the ensemble approach could provide more societal value than a single forecast to end users, especially for low-probability significant events like fog. Deterministically, a forecast close to the ensemble median is particularly helpful. 3) The reliability of probabilistic forecasts can be effectively improved by using a multimodel ensemble instead of a single-model ensemble. For a small ensemble such as the one in this study, the increase in ensemble size is also important in improving probabilistic forecasts, although this effect is expected to decrease with the increase in ensemble size.

Abstract

A vertical distribution formulation of liquid water content (LWC) for steady radiation fog was obtained and examined through the singular perturbation method. The asymptotic LWC distribution is a consequential balance among cooling, droplet gravitational settling, and turbulence in the liquid water budget of radiation fog. The cooling produces liquid water, which is depleted by turbulence near the surface. The influence of turbulence on the liquid water budget decreases with height and is more significant for shallow fogs than for deep fogs. The depth of the region of surface-induced turbulence can be characterized with a fog boundary layer (FBL). The behavior of the FBL bears some resemblance to the surface mixing layer in radiation fog. The characteristic depth of the FBL is thinner for weaker turbulence and stronger cooling, whereas if turbulence intensity increases or cooling rate decreases then the FBL will develop from the ground. The asymptotic formulation also reveals a critical turbulent exchange coefficient for radiation fog that defines the upper bound of turbulence intensity that a steady fog can withstand. The deeper a fog is, the stronger a turbulence intensity it can endure. The persistence condition for a steady fog can be parameterized by either the critical turbulent exchange coefficient or the characteristic depth of the FBL. If the turbulence intensity inside a fog is smaller than the turbulence threshold, the fog persists, whereas if the turbulence intensity exceeds the turbulence threshold or the characteristic depth of the FBL dominates the entire fog bank then the balance will be destroyed, leading to dissipation of the existing fog. The asymptotic formulation has a first-order approximation with respect to turbulence intensity. Verifications with numerical solutions and an observed fog event showed that it is more accurate for weak turbulence than for strong turbulence and that the computed LWC generally agrees with the observed LWC in magnitude.

Abstract

Responding to the call for new verification methods in a recent editorial in Weather and Forecasting, this study proposed two new verification metrics to quantify the forecast challenges that a user faces in decision-making when using ensemble models. The measure of forecast challenge (MFC) combines forecast error and uncertainty information together into one single score. It consists of four elements: ensemble mean error, spread, nonlinearity, and outliers. The cross correlation among the four elements indicates that each element contains independent information. The relative contribution of each element to the MFC is analyzed by calculating the correlation between each element and MFC. The biggest contributor is the ensemble mean error, followed by the ensemble spread, nonlinearity, and outliers. By applying MFC to the predictability horizon diagram of a forecast ensemble, a predictability horizon diagram index (PHDX) is defined to quantify how the ensemble evolves at a specific location as an event approaches. The value of PHDX varies between 1.0 and −1.0. A positive PHDX indicates that the forecast challenge decreases as an event nears (type I), providing creditable forecast information to users. A negative PHDX value indicates that the forecast challenge increases as an event nears (type II), providing misleading information to users. A near-zero PHDX value indicates that the forecast challenge remains large as an event nears, providing largely uncertain information to users. Unlike current verification metrics that verify at a particular point in time, PHDX verifies a forecasting process through many forecasting cycles. Forecasting-process-oriented verification could be a new direction in model verification. The sample ensemble forecasts used in this study are produced from the NCEP global and regional ensembles.

Abstract

An air–soil layer coupled scheme is developed to compute surface fluxes of sensible heat and latent heat from data collected at the Oklahoma Atmospheric Radiation Measurement–Cloud and Radiation Testbed (ARM–CART) stations. This new scheme extends the previous variational method of Xu and Qiu in two aspects: 1) it uses observed standard deviations of wind and temperature together with their similarity laws to estimate the effective roughness length, so the computed fluxes are nonlocal; that is, they contain the contributions of large-eddy motions over a nonlocal area of O(100 km²); and 2) it couples the atmospheric layer with the soil–vegetation layer and uses soil data together with the atmospheric measurements (even at a single level), so the computed fluxes are much less sensitive to measurement errors than those computed by the previous variational method. Surface skin temperature and effective roughness length are also retrieved as by-products by the new method.

The New England High-Resolution Temperature Program seeks to improve the accuracy of summertime 2-m temperature and dewpoint temperature forecasts in the New England region through a collaborative effort between the research and operational components of the National Oceanic and Atmospheric Administration (NOAA). The four main components of this program are 1) improved surface and boundary layer observations for model initialization, 2) special observations for the assessment and improvement of model physical process parameterization schemes, 3) using model forecast ensemble data to improve upon the operational forecasts for near-surface variables, and 4) transfering knowledge gained to commercial weather services and end users. Since 2002 this program has enhanced surface temperature observations by adding 70 new automated Cooperative Observer Program (COOP) sites, identified and collected data from over 1000 non-NOAA mesonet sites, and deployed boundary layer profilers and other special instrumentation throughout the New England region to better observe the surface energy budget. Comparisons of these special datasets with numerical model forecasts indicate that near-surface temperature errors are strongly correlated to errors in the model-predicted radiation fields. The attenuation of solar radiation by aerosols is one potential source of the model radiation bias. However, even with these model errors, results from bias-corrected ensemble forecasts are more accurate than the operational model output statistics (MOS) forecasts for 2-m temperature and dewpoint temperature, while also providing reliable forecast probabilities. Discussions with commerical weather vendors and end users have emphasized the potential economic value of these probabilistic ensemble-generated forecasts.