National Centers For Environmental Prediction Data Research Articles

Using Neural Networks to Predict Hurricane Storm Surge and to Assess the Sensitivity of Surge to Storm Characteristics

AbstractHurricane storm surge represents a significant threat to coastal communities around the world. Here, we use artificial neural network (ANN) models to predict storm surge levels using hurricane characteristics along the US Gulf and East Coasts. The ANN models are trained with storm surge levels from a hydrodynamic model and physical characteristics of synthetic hurricanes which are downscaled from National Centers for Environmental Prediction (NCEP) reanalysis using a statistical‐deterministic hurricane model. The ANN models are able to accurately predict storm surge levels with root‐mean‐square errors (RMSE) below 0.2 m and correlation coefficients > 0.85. The ANN models trained with the NCEP data also show satisfactory accuracy (RMSE below 0.7 m; correlation > 0.7) in predicting storm surge levels for hurricanes downscaled from future climate projections. Once trained, we use the ANN models to assess the sensitivity of storm surge levels to variations in hurricane characteristics and local geophysical features. Progressively stronger maximum wind speeds and larger outer radius sizes independently increase storm surge levels at all locations along the US East and Gulf Coasts. The response of storm surge levels to changes in hurricane translation speed, however, is found to be sensitive to coastal configuration, with increases in hurricane translation speed amplifying (reducing) storm surge levels in open ocean (semi‐enclosed) regions.

Analysis of Rainfall Weather Process in Most of China from 3-6 October, 2021

Heavy rain is a common abnormal weather in China, which is prone to major natural disasters such as floods. By using China National Climate Center’s DERF2.0 (the second-generation product of monthly dynamic extended ensemble prediction) models and NCEP (National Centers for Environmental Prediction) data, and using synoptic and dynamic methods and other research methods, the rainfall weather process in most of China from October 3-6, 2021 is analyzed. The results show that: 1) this process had a long duration, large cumulative rainfall and strong extreme. 2) The warm and wet flow and the cold air intersected in the central and western regions of China and Northeast China, which resulted in a regional rainstorm process within ten days. 3) There was a low-level jet moving from Guizhou and Hunan to the south of Northeast China, bringing a lot of water vapor. To sum up, the rainfall process of this round has a certain relationship with the adjustment of atmospheric circulation.

Analysis of Dust Weather Process in Most Areas of China from January 11 to 16, 2021

The DERF2.0 model of China National Climate Center and NCEP (National Centers for Environmental Prediction) data were used in this paper. Using the knowledge of synoptic principles, the process of dust weather in most parts of China from January 11 to 16, 2021 was analyzed. The results show that: 1) Compared with previous years, this process has a large impact scope and appears earlier. 2) The continuous cold air caused the dust weather process. 3) The surface cold front is conducive to the development of dust. 4) Surface cyclone is conducive to the transport and diffusion of dust.

Wind Resource Potential Assessment and Implication for Climate Change Mitigation: The Case of Bale Zone, South Eastern Ethiopia

Wind power assessments as well as forecast of wind energy production potential are key issues in the wind energy industry. One of the necessary conditions for the development of wind power generation is to choose the optimal site. Alternative energy plays a great role for climate change mitigation, environmental protection and sustainable development. The objective of this study was to assess the distribution of wind resource based on WRF (Weather Research and Forecasting) model and its implication on climate change mitigation in Bale zone, south eastern Ethiopia. In this study, one year wind speed and wind direction at 6- hour intervals at a height of 10, 50 and 100 m were used. The data source is National Centers for Environmental Prediction (NCEP). In addition observational wind speed and wind direction data from National Meteorological Agency (NMA) of Ethiopia from ground based stations were used. The analysis result of the NCEP and NMA data by downscaling the model to 20 km by 20 km spatial resolution enabled to map the wind resource potential sites of Bale Zone applicable for wind mill installation. This study showed that most of the Bale zone areas have significant wind power potential to augment its current power generation. The analysis result revealed that wind resource potential is high during summer than winter season. Have a potential of installed 10,329MW wind capacity in Bale zone. If this potential wind resource will installed, so far environment as an estimation of about more than 5 thousand hectare of forest land per year would be preserved, and subsequently, equivalent amount of about 66,294 of CO 2 would be stored per year. Keywords: Wind speed; Wind direction; Wind power; WRF model; Bale zone DOI: 10.7176/JETP/11-1-01 Publication date: January 31 st 2021

Investigation of precipitation concentration and trends and their potential drivers in the major river basins of Central Asia

Understanding temporal and spatial changes in precipitation has far-reaching implications for watershed development and risk assessment under global climate change. In our study, temporal and spatial variability in precipitation concentration (PC) in Central Asia (CA) from 1980 to 2017, measured with the precipitation concentration index (PCI) and Gini Coefficient (GC), was analyzed using interpolated precipitation from the Climate Prediction Center (CPC) and National Centers for Environmental Prediction (NCEP). The results showed that: (1) interpolated precipitation increased significantly, with significant mutations (P < .5) mainly concentrated between 2000 and 2010; (2) the spatial distribution of the CPC PCI was opposite to that of the NCEP PCI, and fluctuations in CPC GC were larger than that of the NCEP, which indicated that the precipitation data from NCEP was more uniform over the year; (3) for two of three interpolation methods, CPC PC displayed significant mutations in southern CA between 1985 and 2005, while significant (P < .05) mutations in NCEP PC were relatively concentrated in southern CA between 1987 and 2005; (4) although the El Niño Modoki Index (EMI), Pacific Decadal Oscillation (PDO), Nino 3.4, and Southern Oscillation Index (SOI) displayed resonance relationships with CPC PCI, there were no significant abrupt changes in the resonance correlation. In the NCEP data, EMI, PDO, Nino 3.4, and North Atlantic Oscillation (NAO) led to mutations in the resonance relationship with PCI. Our research proved that the change in PC in CA had closely relationship with some teleconnection indices.

Performance assessment of evapotranspiration estimated from different data sources over agricultural landscape in Northern India

Accurate estimation of evapotranspiration is generally constrained due to lack of required hydro-meteorological datasets. This study addresses the performance analysis of Reference Evapotranspiration (ETo) estimated from NASA/POWER, National Center for Environmental Prediction (NCEP) global reanalysis data before and after dynamical downscaling through the Weather Research and Forecasting (WRF) model. The state of the art Hamon’s and Penman-Monteith methods were utilized for the ETo estimation in the Northern India. The performances indices such as Bias, Root Mean Square Error (RMSE) and correlation(r) were calculated, which showed the values 0.242, 0.422 and 0.959 for NCEP data (without downscaling) and 0.230, 0.402,0.969 for the downscaled data respectively. The results indicated that after WRF downscaling, there was some marginal improvement found in the ETo as compared to the without downscaling datasets. However, a better performance was found in the case of NASA/POWER datasets with Bias, RMSE and correlation values of 0.154 0.348 and 0.960 respectively. In overall, the results indicated that the NASA/POWER and WRF downscaled data can be used for ETo estimation, especially in the ungauged areas. However, NASA/POWER is recommended as the ETo calculations are less complicated than those required with NASA/POWER and WRF.

Backcasting long-term climate data: evaluation of hypothesis

Most often than not, incomplete datasets or short-term recorded data in vast regions impedes reliable climate and water studies. Various methods, such as simple correlation with stations having long-term time series, are practiced to infill or extend the period of observation at stations with missing or short-term data. In the current paper and for the first time, the hypothesis on the feasibility of extending the downscaling concept to backcast local observation records using large-scale atmospheric predictors is examined. Backcasting is coined here to contrast forecasting/projection; the former is implied to reconstruct in the past, while the latter represents projection in the future. To assess our hypotheses, daily and monthly statistical downscaling models were employed to reconstruct past precipitation data and lengthen the data period. Urmia and Tabriz synoptic stations, located in northwestern Iran, constituted two case study stations. SDSM and data-mining downscaling model (DMDM) daily as well as the group method of data handling (GMDH) and model tree (Mp5) monthly downscaling models were trained with National Center for Environmental Prediction (NCEP) data. After training, reconstructed precipitation data of the past was validated against observed data. Then, the data was fully extended to the 1948 to 2009 period corresponding to available NCEP data period. The results showed that DMDM performed superior in generation of monthly average precipitation compared with the SDSM, Mp5, and GMDH models, although none of the models could preserve the monthly variance. This overall confirms practical value of the proposed approach in extension of the past historic data, particularly for long-term climatological and water budget studies.

Storm surge hazard in Manila Bay: Typhoon Nesat (Pedring) and the SW monsoon

The Philippines has an average of twenty tropical cyclones entering the Philippine Area of Responsibility every year, with about six to nine making landfall. In September 2011, Typhoon Nesat (Pedring) passed about 200 km north of Manila Bay with maximum sustained wind speeds of 46.3 m/s (CAT 2), yet it was one of the most destructive storms to affect the coastal areas of Metro Manila. In order to further understand the storm surge hazard for Metro Manila, and why Typhoon Nesat was so destructive, both an analysis of historical records and numerical modeling were undertaken. The historical analysis revealed that neither the most intense (with intensity defined as the maximum sustained wind speed of the cyclone) nor the storms that passed closest to the site generated the largest values of surge. Numerical simulations of the storm surge were completed using wind and pressure input data from the Holland vortex model, the Rankine vortex model and reforecast data from the National Center for Environmental Prediction (NCEP) model. NCEP data were found to better represent the wind field in the vicinity of Manila Bay, as it accounted for interaction between Typhoon Nesat and the SW monsoon winds. Neglecting these interactions, the storm surge simulations with analytical vortex models underestimated predicted surge values by more than five times, while the simulations with NCEP data accurately modeled the surge. The study highlights the need for improved storm surge warning systems that account for factors other than the cyclone’s maximum sustained wind speeds, which can affect the severity and duration of surge events.

A merging scheme for constructing daily precipitation analyses based on objective bias‐correction and error estimation techniques

AbstractA new merging scheme (referred to as HL‐OI) was developed to combine daily precipitation data from high‐resolution gauge (HRG) observations, The Climate Prediction Center morphing technique (CMORPH) satellite estimates, and National Centers for Environmental Prediction (NCEP) numerical simulations over China to perform reliable high‐resolution daily precipitation analyses. The scheme is designed using a three‐step strategy of removing systemic biases, reducing random errors, quantitatively estimating error variances, and combining useful information from each data source. First, a cumulative distribution function matching procedure is adopted to reduce biases and provide unbiased background fields for the following merging processes. Second, the developed error estimation algorithm is implemented to quantify both the background and observation errors from the background departures. Third, the bias‐corrected NCEP and CMORPH data are combined with the HRG data using the optimal interpolation (OI) objective analysis technique. The magnitudes and spatial structures of both observation errors and background errors can be estimated successfully. Results of cross‐validation experiments show that the HL‐OI scheme effectively removes most of systemic biases and random errors in the background fields compared to the independent gauge observations and is robust even with imperfect background fields. The HL‐OI merging scheme significantly improves the temporal variations, agreements between the spatial patterns, frequency, and locations of daily precipitation occurrences. When information from gauge observations, satellite estimates, and model simulations are combined simultaneously, the merged multisource analyses perform better than dual‐source analyses. These results indicate that each independent information source of daily precipitation contributes to improving the quality of the final merged analyses under the framework of HL‐OI scheme.

Influence of availability of TAO data on NCEP ocean data assimilation systems along the equatorial Pacific

AbstractIt is reasonable to assume that the quality of an ocean reanalysis will depend not only on the ocean data assimilation system but also on the availability of observational data. Among the data assimilated in the tropical Pacific, observations from the Tropical Atmosphere Ocean (TAO) moored array are an important contributor. Due to various reasons, in the last couple of years, TAO had a very low data delivery rate at some of the mooring sites. In this work, we examined the influence of the availability of in situ TAO data from all 10 equatorial mooring sites in 1999–2014 by comparing ocean temperature from two data assimilation systems at National Centers for Environmental Prediction (NCEP): the Global Ocean Data Assimilation System (GODAS) and the Climate Forecast System Reanalysis (CFSR). Our working hypothesis is that missing of in situ observational data should lead to less observational constraint, and consequently, larger divergence between ocean reanalyses. Compared with periods of no in situ TAO observations, the disagreements of ocean temperature between CFSR and GODAS are indeed smaller when in situ TAO observations are available. The disagreements are largest along the thermocline due to its being the region of strongest variability. Thus, it is suggested that without the constraint of in situ TAO observations, the inconsistency between the ocean reanalyses from two NCEP data assimilation systems is larger, highlighting the contribution of TAO observations. Moreover, it is also suggested that the disagreement of ocean temperature between GODAS and CFSR is mainly caused by the difference of the models used in the assimilation systems and the impact of in situ TAO availability plays a secondary, but important role. To improve the quality of the assimilation products, it is necessary both to reduce model bias and to assimilate more high‐quality data.

Mesoscale modeling study of the interactions between aerosols and PBL meteorology during a haze episode in Jing–Jin–Ji (China) and its nearby surrounding region – Part 1: Aerosol distributions and meteorological features

Abstract. The urbanized region ofJing(Beijing)-Jin(Tianjin)-Ji (alias of Hebei province) and its nearby surrounding region (3JNS) is becoming China's most polluted area by haze, exceeding even the Yangtze and Pearl river deltas. Aside from pollutant emission, the meteorology of the planetary boundary layer (PBL) is the most important factor affecting haze pollution. Focusing on July 2008, the aerosol optical properties and PBL meteorology features closely related to haze formation were simulated in the 3JNS region using an online atmospheric chemical transport model. The relationship between regional PBL meteorology, PM2.5, and haze is discussed. Model results accurately simulated the aerosol optical depth (AOD), single scattering albedo (SSA) and asymmetry parameter (ASY), validated by comparison with observations from the MODerate Resolution Imaging Spectroradiometer (MODIS), the China Aerosol Remote Sensing NETwork (CARSNET) and the Aerosol Robotic NETwork (AERONET). Modeled PBL wind speeds showed reasonable agreement with those from the National Centers for Environmental Prediction (NCEP) Reanalysis 2. A monthly mean AOD value as high as 1.2 was found from both model and observations, with a daily mean larger than 2.0 during haze episodes in the 3JNS region. Modeled and observed SSA values of 0.90–0.96 and ASY values of 0.72–0.74 demonstrated the high scattering characteristic of summer aerosols in this region. PBL wind speeds from modeled and NCEP data both showed a reversing trend of PM2.5 variation, illustrating the importance of the "PBL window shadow" in haze formation. Turbulence diffusion and PBL height had opposite phases to surface PM2.5, indicating that lower PBL height and weaker PBL turbulence diffusion are essential to haze formation. It is noted that homogeneous air pressure does not occur at the surface, but at an 850–950 hPa height during the haze episode. The momentum transmitting downward of the cold air from above the PBL to the low PBL and surface lead to an increase in surface wind speeds and haze dispersal.

Statistical and in-situ validations of the ASTER spectral emissivity product at Railroad Valley, Nevada, USA

This study investigated the accuracy of the surface emissivity generated as an Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) standard product applying the Temperature Emissivity Separation (TES) algorithm. We statistically compared the emissivity values using 61 daytime and 25 nighttime ASTER scenes at the Railroad Valley playa, USA from 2000 to 2012. The spectral emissivity was consistent within ±0.007 at bands 13 (10.60μm) and 14 (11.30μm), although the emissivity at bands 10 to 12 (8.30, 8.65, and 9.10μm) varied temporally, probably due to surface condition changes and errors in atmospheric correction. The atmospheric correction based on the climatology data resulted in poor accuracy in TES-retrieved emissivity for nighttime data because the contribution of the atmospheric effect on the at-sensor radiance increases due to decreased surface radiance in nighttime. For the National Centers for Environmental Prediction (NCEP) data, the agreement, which is comparable to the nominal accuracy (=0.015), between the emissivity based on the day and night ASTER data acquired on the same day suggested that NCEP data is sufficiently accurate to correct for the atmospheric effect on the ASTER data under nominally dry atmospheric condition. Although the spatial scales differ between ASTER data and laboratory data, the satellite and ground emissivity agreed within ±0.013. Despite the similarity of the spectral emissivity patterns, the ASTER emissivity was as much as 0.066 lower than the field emissivity at 8.30μm due perhaps to the roughness effect on the field emissivity.

A method for monitoring the on-orbit performance of a satellite sensor infrared window band using oceanic drifters

In this study, we propose a method to monitor the on-orbit performance of a satellite sensor thermal emissive band (TEB) using oceanic drifters and National Centers for Environmental Prediction (NCEP) data. Based on the radiance simulated using oceanic drifter data, NCEP data, and a radiative transfer model, we primarily aim to develop a method for the real-time monitoring of the on-orbit performance of a satellite sensor thermal emissive band. One month of Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) L1b radiance data from Channels 31 and 32 are used as proxy data to test this method. In this paper, we propose some improvements to the cloud test algorithm for this monitoring method, thereby significantly enhancing the quality of this method. We describe the sensitivity tests conducted to identify the important sources of errors. Our improvements eliminate the cases contaminated by clouds and influenced by non-uniform distribution of seawater temperature. Comparisons with Aqua MODIS data reveal systematic underestimations (0.48 ± 0.97 K for Channel 31, 0.36 ± 1.05 K for Channel 32) of this method. Our sensitivity analysis also reveals that surface emissivity and drifter temperature are likely to make the greatest contributions to the total uncertainty of this method. We should therefore correct systematical errors in the real-time monitoring algorithm for other satellite TEB sensors.

An Analysis for the Accuracy of Tropospheric Zenith Delay Calculated From ECMWF/NCEP Data Over Asia

AbstractThe tropospheric delay is an important error source for the satellite navigation and positioning, and the numerical forecast data derived from meteorological instruments can be used to calculate the tropospheric delay. In this study, the zenith tropospheric delay (ZTD) observed from 49 Global Positioning System (GPS) sites distributed in Asia was applied to assess the availability and accuracy for ZTD calculated from the European Center for Medium‐Range Weather Forecasts (ECMWF) and the United States National Centers for Environmental Prediction (NCEP). The practicability of ECMWF/NCEP data in Asia and the relation between the resolution and the accuracy for ECMWF were also studied. The results show that relative to GPS observed ZTD (GPS ZTD), the bias and rms for the ECMWF data are –1.0 cm and 2.7 cm, respectively, which are better than that for the NCEP data and can be used for ZTD study and application with high accuracy. The bias and rms for the NCEP forecast data are 2.4 cm and 6.8 cm, which are sufficed for the tropospheric delay correction in the real‐time GNSS navigation and positioning. The bias and rms exhibit a seasonal variation, which are generally of larger values in summer months and smaller values in winter months; however, the relation between the bias and rms and the latitude is not obvious, but the rms is decreasing while the altitude increasing. In addition, the daily bias and rms in eastern Asia in summer and in southern Asia in winter have a relatively larger variation. The resolutions of 2.5 degree and 0.5 degree for the ECMWF data are compared; the rms for the latter is decreased by 1~5 mm than the former. These results provide a reference for the availability and accuracy of the ECMWF/NCEP data used for calculating the tropospheric delay in the space geodesy, InSAR, navigation and positioning.

Coupling statistical and dynamical methods for spatial downscaling of precipitation

The resolution of General Circulation Models (GCMs) is too coarse for climate change impact studies at the catchment or site-specific scales. To overcome this problem, both dynamical and statistical downscaling methods have been developed. Each downscaling method has its advantages and drawbacks, which have been described in great detail in the literature. This paper evaluates the improvement in statistical downscaling (SD) predictive power when using predictors from a Regional Climate Model (RCM) over a GCM for downscaling site-specific precipitation. Our approach uses mixed downscaling, combining both dynamic and statistical methods. Precipitation, a critical element of hydrology studies that is also much more difficult to downscale than temperature, is the only variable evaluated in this study. The SD method selected here uses a stepwise linear regression approach for precipitation quantity and occurrence (similar to the well-known Statistical Downscaling Model (SDSM) and called SDSM-like herein). In addition, a discriminant analysis (DA) was tested to generate precipitation occurrence, and a weather typing approach was used to derive statistical relationships based on weather types, and not only on a seasonal basis as is usually done. The existing data record was separated into a calibration and validation periods. To compare the relative efficiency of the SD approaches, relationships were derived at the same sites using the same predictors at a 300km scale (the National Center for Environmental Prediction (NCEP) reanalysis) and at a 45km scale with data from the limited-area Canadian Regional Climate Model (CRCM) driven by NCEP data at its boundaries. Predictably, using CRCM variables as predictors rather than NCEP data resulted in a much-improved explained variance for precipitation, although it was always less than 50 % overall. For precipitation occurrence, the SDSM-like model slightly overestimated the frequencies of wet and dry periods, while these were well-replicated by the DA-based model. Both the SDSM-like and DA-based models reproduced the percentage of wet days, but the wet and dry statuses for each day were poorly downscaled by both approaches. Overall, precipitation occurrence downscaled by the DA-based model was much better than that predicted by the SDSM-like model. Despite the added complexity, the weather typing approach was not better at downscaling precipitation than approaches without classification. Overall, despite significant improvements in precipitation occurrence prediction by the DA scheme, and even going to finer scales predictors, the SD approach tested here still explained less than 50 % of the total precipitation variance. While going to even smaller scale predictors (10–15 km) might improve results even more, such smaller scales would basically transform the direct outputs of climate models into impact models, thus negating the need for statistical downscaling approaches.

Comparison between different sources of atmospheric profiles for land surface temperature retrieval from single channel thermal infrared data

Different sources of atmospheric water vapor and temperature profiles were used with a radiative transfer model for retrieving land surface temperature (LST) from thermal infrared remote sensing data with the so-called single channel (SC) method. Retrieved LSTs were compared to concurrent ground measurements over homogeneous rice fields to assess the accuracy of the atmospheric profiles. These included radiosonde balloons launched at the test site near-concurrently to satellite overpasses, re-analysis profiles from the National Centers for Environmental Prediction (NCEP), and satellite sounder products from the Atmospheric Infrared Sounder (AIRS) and the Moderate Imaging Spectroradiometer (MODIS; MOD07 product). SC LSTs were computed for Enhanced Thematic Mapper+ (ETM+), Advanced Spaceborne Thermal Emission and Reflection radiometer (ASTER), MODIS, and Advanced Along-Track Scanning Radiometer (AATSR). Results show that radiosonde profiles provided the best agreement between ground-measured and satellite-derived LSTs, with root mean square difference (RMSD) better than 1.0 K and biases within ± 0.5 K for most of the cases. As an alternative to radiosonde profiles, NCEP and MOD07 data yielded reasonable results with RMSDs around 1.0 K, although LSTs derived from MOD07 profiles showed a slight overestimation (0.5 to 1.0 K) of the ground LSTs. AIRS profiles usually underestimated the ground LSTs by 1 to 2 K, probably due to the large temporal gap (2–3 h) with the other satellite measurements. We propose a test to assess the suitability of atmospheric profiles applicable to sensors with bands at 11 and 12 μm in the split-window. So this test plus the SC method may be called split-window SC algorithm, being significantly different from the simple SC method. It implies the calculation of the difference between the LST derived from both bands (T ig − T jg), which should be close to zero if the atmospheric profile is accurate and the surface emissivity is well known. A small range of T ig − T jg values around zero can be set for which the LST derived at 11 μm is accurate. Using only the profiles passing the test, LSTs derived from MODIS band 31 agreed with the ground data with a mean bias of − 0.1 K (ground minus satellite) and RMSD of 0.6 K, while AATSR band at 11 μm yielded a − 0.1 K bias and RMSD = 0.5 K.

Assessment of ZTD derived from ECMWF/NCEP data with GPS ZTD over China

The accuracy and feasibility of computing the zenith tropospheric delays (ZTDs) from data of the European Center for Medium-Range Weather Forecasts (ECMWF) and the United States National Centers for Environmental Prediction (NCEP) are studied. The ZTDs are calculated from ECMWF/NCEP pressure-level data by integration and from the surface data with the Saastamoinen model method and then compared with the solutions measured from 28 global positioning system (GPS) stations of the Crustal Movement Observation Network of China (CMONOC) for 1 year. The results are as follows: (1) the error of the integration method is 1---3 cm less than that of the Saastamoinen model method. The agreement between the ECMWF ZTD and GPS ZTD is better than that between NCEP ZTD and GPS ZTD; (2) the bias and root mean square difference (RMSD), especially the latter, have a seasonal variation, and the RMSD decreases with increasing altitude while the variation with latitude is not obvious; and (3) when using the full horizontal resolution of 0.5° × 0.5° of the ECMWF meteorological data in place of a reduced 2.5° × 2.5° grid, the mean RMSD between GPS and ECMWF ZTD decreases by 4.5 mm. These results illuminated the accuracy and feasibility of computing the tropospheric delays and establishing the ZTD prediction model over China for navigation and positioning with ECMWF and NCEP data.

Numerical simulations of spreading of the Persian Gulf outflow into the Oman Sea

Abstract. A three dimensional numerical model namely POM (Princeton Ocean Model) and observational data are used to study the Persian Gulf outflow structure and its spreading pathways during 1992. In the model, the monthly wind speed data were taken from ICOADS (International Comprehensive Ocean-Atmosphere Data Set) and the monthly SST (sea surface temperatures) were taken from AVHRR (Advanced Very High Resolution Radiometer) with the addition of monthly net shortwave radiations from NCEP (National Center for Environmental Prediction). The mean monthly precipitation rates from NCEP data and the calculated evaporation rates are used to impose the surface salinity fluxes. At the open boundaries the temperature and salinity were prescribed from the mean monthly climatological values from WOA05 (World Ocean Atlas 2005). Also the four major components of the tide were prescribed at the open boundaries. The results show that the outflow mainly originates from two branches at different depths in the Persian Gulf. The permanent branch exists during the whole year deeper than 40 m along the Gulf axis and originates from the inner parts of the Persian Gulf. The other seasonal branch forms in the vicinity of the shallow southern coasts due to high evaporation rates during winter. Near the Strait of Hormuz the two branches join and form the main outflow source water. The results of simulations reveal that during the winter the outflow boundary current mainly detaches from the coast well before Ras Al Hamra Cape, however during summer the outflow seems to follow the coast even after this Cape. This is due to a higher density of the colder outflow that leads to more sinking near the coast in winter. Thus, the outflow moves to a deeper depth of about 500 m (for which some explanations are given) while the main part detaches and spreads at a depth of about 300 m. However in summer it all moves at a depth of about 200–250 m. During winter, the deeper, stronger and wider outflow is more affected by the steep topography, leading to separation from the coast. While during summer, the weaker and shallower outflow is less influenced by bottom topography and so continues along the boundary.

Relationship between North Pacific SST anomalies and the atmospheric circulation anomalies in January 2008

Based on the NCEP (National Centers for Environmental Prediction) data, the relationship between the Sea Surface Temperature Anomalies (SSTAs) in the North Pacific and the atmospheric circulation anomalies in January 2008 is analyzed in this study. The SSTA mode most correlated with the Geopotential Height anomalies (GHAs) in January 2008 in the North Pacific exhibited a basin-wide horseshoe pattern with a warm center in November 2007. This persistent SSTA pattern would induce positive GHAs in the Aleutian Low area and East Asia and the northward extension of the West Pacific Subtropical High in January 2008 by maximum diabatic heating in the atmosphere over the Kuroshio Oyashio Extension (KOE) area, leading to the occurence of the circumpolar trough-ridge wave train anomaly in January 2008.

Stratospheric semi-decadal oscillations in NCEP data

Abstract. An analysis of the National Centers for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) data is presented to provide a more complete description of the stratospheric 5-year semi-decadal (SD) oscillation (Mayr et al., 2007). The zonal-mean temperature and zonal wind data from the Atmospheric Research R-1 analysis are employed, covering the years from 1962 to 2002 in the altitude range from 10 to 30 km. For diagnostic purposes, the data are separated into the hemispherically symmetric and anti-symmetric components, and spectral analysis is applied to identify the signatures of the SD oscillations. Through the synthesis or filtering of spectral features, the SD modulations of the annual oscillation (AO) and quasi-biennial oscillation (QBO) are delineated. In agreement with the earlier findings, the magnitude of the SD oscillation is more pronounced when the 30-month QBO dominates during the years from 1975 to 1995. This is consistent with results from a numerical model, which shows that such a QBO generates the SD oscillation through interaction with the 12-month AO. In the zonal winds, the SD oscillation in the NCEP data is confined to equatorial latitudes, where it modulates the symmetric AO and QBO by about 5 m/s below 30 km. In the temperature data, the effect is also seen around the equator, but it is much larger at polar latitudes where the SD oscillation produces variations as large as 2 K. Our data analysis indicates that the SD oscillation is mainly hemispherically symmetric, and it appears to originate at equatorial latitudes where most of the energy resides.