NCEP Climate Forecast Research Articles

Extreme value analysis of wave climate in Chesapeake Bay

A thirty-seven year wave hindcast (1979–2015) in Chesapeake bay using NCEP's Climate Forecast System Reanalysis (CFSR) wind is presented. The long-term significant wave heights are generated by the third-generation nearshore wave model SWAN, which is validated using the wave height measurements at buoy stations in the bay. The simulated wave heights are analyzed to characterize their temporal and spatial variabilities as well as long-term changing trends by using an Empirical Orthogonal Function (EOF) analysis and an empirical cumulative distribution function approach. Seasonal variability as well as extreme storm effects on significant wave heights are revealed in the first mode of principle component. Then, an extreme value analysis based on generalized extreme value and generalized Pareto distribution functions is applied to evaluate design wave heights with different return periods. The effects of key parameters including threshold value, time span and data length on the design wave heights are extensively studied. Through the comparisons of different distribution functions evaluated by Bayesian Information Criterion and Akaike Information Criterion, it is found that Gamma distribution function and generalized extreme value analysis provide the best fit for annual and monthly data, while generalized Pareto distribution function gives the best fit when peak-over-threshold analysis is conducted.

A hybrid downscaling model for winter temperature over northeast China

ABSTRACTA hybrid downscaling model is established to forecast the winter temperature at 182 stations over northeast China based on the year‐to‐year increment approach (differences in variables between the current and previous year). As winter temperature over China is related to general circulation and oceanic circulation signals, the three most suitable predictors at specific regions are ultimately considered in our model after analysing the physical processes involved. Accordingly, winter sea level pressure (SLP) from version 2 of the NCEP's Climate Forecast System (CFSv2) is used as a current‐year predictor, along with two other previous‐year predictors – sea surface temperature (SST) in August and sea‐ice concentration (SIC) in November, from observation. Four separate downscaling schemes are then proposed, three of which involve just one predictor (i.e. SLP‐scheme, SST‐scheme, and SIC‐scheme), and the fourth involves all of the predictors [i.e. hybrid downscaling (HD)‐scheme]. We evaluate the schemes, through cross‐validation, by examining the spatial and temporal anomaly correlation coefficients (ACCs) and reduction in root‐mean‐square error percentage (RMSEP). Considerable evidence is found that all of the schemes improve the skill in predicting winter temperature over northeast China, especially the HD‐scheme, as compared with real‐time CFSv2 forecasting. The 33‐year spatial ACC average for the HD‐scheme rises approximately from −0.017 to 0.30, which is much larger than the threshold value of 0.19, showing statistical significance at the 99% confidence level. Additionally, the temporal RMSEP (i.e. the difference between the RMSE of the CFSv2 outputs and the downscaling results) decreases by more than 30% for all four schemes. Moreover, the cold winter temperature pattern over northeast China in 2013 is reproduced well by the HD‐scheme.

Assessing surface heat fluxes in atmospheric reanalyses with a decade of data from the NOAA Kuroshio Extension Observatory

AbstractPrevious studies have found large biases and uncertainties in the air‐sea fluxes from Numerical Weather Prediction model reanalyses, which must be identified and reduced in order to make progress on weather and climate predictions. Here air‐sea heat fluxes from NOAA Kuroshio Extension Observatory (KEO) measurements are used to assess two new reanalyses, NCEP's Climate Forecast System Reanalysis (CFSR) and ECMWF Reanalysis‐Interim (ERA‐I), suggesting that these two new generation reanalyses have significantly improved. In both reanalyses, all four flux components (sensible and latent heat flux and net longwave and shortwave radiation) are highly correlated with observation, with the correlation of total net surface heat fluxes above 0.96. Although errors of the net surface heat flux have significantly reduced from previous reanalyses, the Root Mean Square Errors (RMSEs) and biases remain high especially for CFSR: the RMSEs of CFSR and ERA‐I are reduced by 25–30% to 64 and 61 W/m2, respectively, while biases are reduced by 40–60% to 28 and 20 W/m2. But CFSR overestimates the winter heat release by 90 W/m2. The main cause of biases is the latent heat flux, while RMS errors are primarily due to latent heat flux and shortwave radiation errors. Both reanalyses overestimate the wind speed associated with winter storms and underestimate specific humidity in summer. The ERA‐I latent heat flux and its total net surface heat flux are however closer to observation. It is the bulk algorithm in CFSR that is found to be mainly responsible for overestimates of winter heat release in CFSR.

Evaluation and Intercomparison of Cloud Fraction and Radiative Fluxes in Recent Reanalyses over the Arctic Using BSRN Surface Observations

Abstract With continual advancements in data assimilation systems, new observing systems, and improvements in model parameterizations, several new atmospheric reanalysis datasets have recently become available. Before using these new reanalyses it is important to assess the strengths and underlying biases contained in each dataset. A study has been performed to evaluate and compare cloud fractions (CFs) and surface radiative fluxes in several of these latest reanalyses over the Arctic using 15 years (1994–2008) of high-quality Baseline Surface Radiation Network (BSRN) observations from Barrow (BAR) and Ny-Alesund (NYA) surface stations. The five reanalyses being evaluated in this study are (i) NASA's Modern-Era Retrospective analysis for Research and Applications (MERRA), (ii) NCEP's Climate Forecast System Reanalysis (CFSR), (iii) NOAA's Twentieth Century Reanalysis Project (20CR), (iv) ECMWF's Interim Reanalysis (ERA-I), and (v) NCEP–Department of Energy (DOE)'s Reanalysis II (R2). All of the reanalyses show considerable bias in reanalyzed CF during the year, especially in winter. The large CF biases have been reflected in the surface radiation fields, as monthly biases in shortwave (SW) and longwave (LW) fluxes are more than 90 (June) and 60 W m−2 (March), respectively, in some reanalyses. ERA-I and CFSR performed the best in reanalyzing surface downwelling fluxes with annual mean biases less than 4.7 (SW) and 3.4 W m−2 (LW) over both Arctic sites. Even when producing the observed CF, radiation flux errors were found to exist in the reanalyses suggesting that they may not always be dependent on CF errors but rather on variations of more complex cloud properties, water vapor content, or aerosol loading within the reanalyses.

Monitoring and predicting the 2007 U.S. drought

Severe droughts developed in the West and Southeast of the U.S. starting early in 2007. The development of the droughts is well monitored and predicted by our model‐based Drought Monitoring and Prediction System (DMAPS). Using the North America Land Data Assimilation System (NLDAS) realtime meteorological forcing and the Variable Infiltration Capacity (VIC) land surface model, DMAPS is capable of providing a quantitative assessment of the drought in near realtime. Using seasonal climate forecasts from NCEP's Climate Forecast System (CFS) as one input, DMAPS successfully predicted the evolution of the droughts several months in advance. The realtime monitoring and prediction of drought with the system will provide invaluable information for drought preparation and drought impact assessment at national and local scales.