Global Data Assimilation System Research Articles

Can a Moderate-Resolution Limited-Area Data Assimilation System Add Value to the Global Analysis of Tropical Cyclones?

AbstractThis study investigates the benefits of employing a limited-area data assimilation (DA) system to enhance lower-resolution global analyses in the northwest Pacific tropical cyclone (TC) basin. Numerical experiments are carried out with a global analysis system at horizontal resolution T62 and a limited-area analysis system at resolutions from 200 to 36 km. The global and limited-area DA systems, which are both based on the local ensemble transform Kalman filter algorithm, are implemented using a unique configuration, in which the global DA system provides information about the large-scale analysis and background uncertainty to the limited-area DA system. The limited-area analyses of the storm locations are, on average, more accurate than those from the global analyses, but increasing the resolution of the limited-area system beyond 100 km has little benefit. Two factors contribute to the higher accuracy of the limited-area analyses. First, the limited-area system improves the accuracy of the location estimates for strong storms, which is introduced when the background is updated by the global assimilation. Second, it improves the accuracy of the background estimate of the storm locations for moderate and weak storms. Improvements in the steering flow analysis as a result of increased resolution are modest and short lived in the forecasts. Limited-area track forecasts are more accurate, on average, than global forecasts, independently of the strength of the storms up to five days. This forecast improvement is due to the more accurate analysis of the initial position of storms and the better representation of the interactions between the storms and their immediate environment.

Operational Evapotranspiration Mapping Using Remote Sensing and Weather Datasets: A New Parameterization for the SSEB Approach

AbstractThe increasing availability of multi‐scale remotely sensed data and global weather datasets is allowing the estimation of evapotranspiration (ET) at multiple scales. We present a simple but robust method that uses remotely sensed thermal data and model‐assimilated weather fields to produce ET for the contiguous United States (CONUS) at monthly and seasonal time scales. The method is based on the Simplified Surface Energy Balance (SSEB) model, which is now parameterized for operational applications, renamed as SSEBop. The innovative aspect of the SSEBop is that it uses predefined boundary conditions that are unique to each pixel for the “hot” and “cold” reference conditions. The SSEBop model was used for computing ET for 12 years (2000‐2011) using the MODIS and Global Data Assimilation System (GDAS) data streams. SSEBop ET results compared reasonably well with monthly eddy covariance ET data explaining 64% of the observed variability across diverse ecosystems in the CONUS during 2005. Twelve annual ET anomalies (2000‐2011) depicted the spatial extent and severity of the commonly known drought years in the CONUS. More research is required to improve the representation of the predefined boundary conditions in complex terrain at small spatial scales. SSEBop model was found to be a promising approach to conduct water use studies in the CONUS, with a similar opportunity in other parts of the world. The approach can also be applied with other thermal sensors such as Landsat.

Very high cloud detection in more than two decades of HIRS data

This paper reports on the use of High‐resolution Infrared Radiation Sounder (HIRS) measurements to infer the presence of upper tropospheric and lower stratospheric (UT/LS) clouds. UT/LS cloud detection is based on the fact that, when viewing an opaque UT/LS cloud that fills the sensor field of view, positive lapse rates above the tropopause cause a more absorbing CO2 or H2O‐sensitive spectral band to measure a brightness temperature warmer than that of a less absorbing or nearly transparent infrared window spectral band. The HIRS sensor has flown on 16 polar‐orbiting satellites from TIROS‐N through NOAA‐19 and Metop‐A and –B, forming the only 30 year record that includes H2O and CO2‐sensitive spectral bands enabling the detection of these UT/LS clouds. Comparison with collocated Cloud‐Aerosol Lidar with Orthogonal Polarization data reveals that 97% of the HIRS UT/LS cloud determinations are within 2.5 km of the tropopause (defined as the coldest level in the National Centers for Environmental Prediction Global Data Assimilation System); more clouds are found above the tropopause than below. From NOAA‐14 data spanning 1995 through 2005, we find indications of UT/LS clouds in 0.7% of the observations from 60N to 60S using CO2 absorption bands; however, in the region of the Inter‐Tropical Convergence Zone (ITCZ), this increases to 1.7%. During El Niño years, UT/LS clouds shift eastward out of their normal location in the western Pacific region. Monthly trends from 1987 through 2011 using data from NOAA‐10 onwards show decreases in UT/LS cloud detection in the region of the ITCZ from 1987 until 1996, increases until 2001, and decreases thereafter.

The MACC reanalysis: an 8 yr data set of atmospheric composition

Abstract. An eight-year long reanalysis of atmospheric composition data covering the period 2003–2010 was constructed as part of the FP7-funded Monitoring Atmospheric Composition and Climate project by assimilating satellite data into a global model and data assimilation system. This reanalysis provides fields of chemically reactive gases, namely carbon monoxide, ozone, nitrogen oxides, and formaldehyde, as well as aerosols and greenhouse gases globally at a horizontal resolution of about 80 km for both the troposphere and the stratosphere. This paper describes the assimilation system for the reactive gases and presents validation results for the reactive gas analysis fields to document the data set and to give a first indication of its quality. Tropospheric CO values from the MACC reanalysis are on average 10–20% lower than routine observations from commercial aircrafts over airports through most of the troposphere, and have larger negative biases in the boundary layer at urban sites affected by air pollution, possibly due to an underestimation of CO or precursor emissions. Stratospheric ozone fields from the MACC reanalysis agree with ozonesondes and ACE-FTS data to within ±10% in most seasons and regions. In the troposphere the reanalysis shows biases of −5% to +10% with respect to ozonesondes and aircraft data in the extratropics, but has larger negative biases in the tropics. Area-averaged total column ozone agrees with ozone fields from a multi-sensor reanalysis data set to within a few percent. NO2 fields from the reanalysis show the right seasonality over polluted urban areas of the NH and over tropical biomass burning areas, but underestimate wintertime NO2 maxima over anthropogenic pollution regions and overestimate NO2 in northern and southern Africa during the tropical biomass burning seasons. Tropospheric HCHO is well simulated in the MACC reanalysis even though no satellite data are assimilated. It shows good agreement with independent SCIAMACHY retrievals over regions dominated by biogenic emissions with some anthropogenic input, such as the eastern US and China, and also over African regions influenced by biogenic sources and biomass burning.

Assimilation of Infrared Radiances in the Context of Observing System Simulation Experiments

AbstractThe Observing System Simulation Experiment (OSSE) capability developed at Environment Canada allows simulation of all observation types currently used operationally as well as future data types. Its infrastructure, based on the operational global data assimilation system used at the Canadian Meteorological Centre, was recently enhanced to conduct data assimilation experiments for two future satellite missions. This study presents a subcomponent of that system, focusing on the assimilation of infrared radiances from the Atmospheric Infrared Sounder (AIRS) and Infrared Atmospheric Sounding Interferometer (IASI) instruments. The goal is to realistically simulate the radiance observations and to reproduce statistical characteristics of these data seen in the real system, notably background and analysis departures. Care is taken to emulate the operational quality control procedures leading to the assimilation of clear radiances, which implies radiance simulation for all-sky conditions. It is found that the standard deviation of the Gaussian random perturbation applied to the simulated observations should be close to that of the radiometric noise level for sounding channels in the 13.0–14.5-μm region but that it should be significantly higher for water vapor channels in the 5.5–6.7-μm region. The study also allows evaluation of residual biases linked to cloud contamination. For atmospheric window channels, that bias can reach −0.4 K. It reduces rapidly with peak height of the channel response function. This suggests that improvements are needed in low-cloud detection. The realism of the OSSE is further demonstrated through the shown impact consistency of AIRS and IASI radiance assimilation in forecasts up to 5 days from the separate use of simulated and real observations.

Wind Speed Retrieval Based on Sea Surface Roughness Measurements from Spaceborne Microwave Radiometers

AbstractWind speed is the main factor responsible for the increase in ocean thermal emission because sea surface emissivity strongly depends on surface roughness. An alternative approach to estimate the surface wind speed (SWS) as a function of surface roughness is developed in this study. For the sea surface emissivity, the state-of-the-art forward Fast Microwave Emissivity Model, version 3 (FASTEM-3), which is applicable for a wide range of microwave frequencies at incidence angles of less than 60°, is used. Special Sensor Microwave Imager and Advanced Microwave Scanning Radiometer (AMSR-E) observations are simulated using FASTEM-3 and the Global Data Assimilation and Prediction System operated by the Korea Meteorological Administration. The performance of the SWS retrieval algorithm is assessed by comparing its SWS output to that of the Global Data Assimilation System operated by the National Centers for Environmental Prediction. The surface roughness is computed using the Hong approximation and characteristics of the polarization ratio. When compared with the Tropical Atmosphere–Ocean data, the bias and root-mean-square error (RMSE) of the SWS outputs from the proposed wind speed retrieval algorithm were found to be 0.32 m s−1 (bias) and 0.37 m s−1 (RMSE) for the AMSR-E 18.7-GHz channel, 0.38 m s−1 (bias) and 0.42 m s−1 (RMSE) for the AMSR-E 23.8-GHz channel, and 0.45 m s−1 (bias) and 0.49 m s−1 (RMSE) for the AMSR-E 36.5-GHz channel. Consequently, this research provides an alternative method to retrieve the SWS with minimal a priori information on the sea surface.

The Inclusion of IWV Estimates from AIRS/AMSU and SSM/I Sensors into the CPTEC/INPE Global Data Assimilation System

Abstract Water vapor plays a crucial role in atmospheric processes and its distribution is associated with cloud-cover fraction and rainfall. The inclusion of integrated water vapor (IWV) estimates in numerical weather prediction improves the vertical structure of the humidity analysis and consequently contributes to obtaining a more realistic atmospheric state. Currently, satellite remote sensing is the most important source of humidity measurements in the Southern Hemisphere, providing information with good horizontal resolution and global coverage. In this study, the inclusion of IWV retrieved from the Atmospheric Infrared Sounder/Advanced Microwave Sounding Unit-A (AIRS/AMSU) and Special Sensor Microwave Imager (SSM/I) were investigated as additional information in the Physical-space Statistical Analysis System (PSAS), which is the operational data assimilation system at the Center for Weather Forecasting and Climate Studies of the Brazilian National Institute for Space Research (CPTEC/INPE). Experiments were carried out with and without the assimilation of IWV values from both sensors. Results show that, in general, the IWV assimilation reduces the error in short-range forecasts of humidity profile, particularly over tropical regions. In these experiments, an analysis of the impact of the inclusion of IWV values from SSM/I and AIRS/AMSU sensors was done. Results indicated that the impact of the SSM/I values is significant over high-latitude oceanic regions in the Southern Hemisphere, while the impact of AIRS/AMSU values is more significant over continental regions where surface measurements are scarce, such as the Amazonian region. In that area the assimilation of IWV values from the AIRS/AMSU sensor shows a tendency to reduce the overestimate of the precipitation in short-range forecasts.

A Statistical Downscaling Approach of NCEP/NCAR Reanalysis Temperature Data

Near-surface air temperature is an important controlling parameter for land surface processes, and is critical to ecological, environmental and hydrological modeling. Temperature records observed at meteorological stations have been widely used, but there has been an increasing need for temperature data in grid for modeling purposes. Although grid temperature can be estimated from in-situ temperature records using interpolation algorithm, low accuracy have been reported due to limited ground stations and their clustering distribution, especially when there were insufficient sites to represent all land cover types and terrain conditions in the area. NCEP/NCAR reanalysis project uses a frozen state-of-art global data assimilation system and a database as complete as possible. Although the NCEP/NCAR data has a coarse resolution(0.5 degree), it provides global, consistent, and long term estimation of climate variables. This paper presents a downscaling approach to derive monthly temperature at 1km resolution from the NCEP/NCAR by utilizing derived relationships between monthly aggregated NCEP/NCAR temperature and other ground elements, i.e., terrain, vegetation and geographic locations. Regression tree model was chosen to detect the possible relationships. Monthly temperature with 1km resolution for China land area from 2000 to 2010 has been produced using the approach. The final predicted temperatures were compared with observed records at 380 meteorological stations in China. The results indicate that the downscaled estimations can represent spatial distribution and trends and the magnitude of inter-month temperature with R2 ranging from 0.861 to 0.95, and RMSE from 1.88℃ to 2.681℃.

분석자료의 분해능과 3DVAR 적용에 따른 WRF모의 민감도: 사례 연구

This study aims at examining the sensitivity of numerical simulations to the resolution of initial and boundary data, and to an application of WRF (Weather Research and Forecasting) 3DVAR (Three Dimension Variational data Assimilation). To do this, we ran the WRF model by using GDAS (Global Data Assimilation System) FNL (Final analyses) and the KLAPS (Korea Local Analysis and Prediction System) analyses as the WRF's initial and boundary data, and by using an initial field made by assimilating the radar data to the KLAPS analyses. For the sensitivity experiment, we selected a heavy rainfall case of 21 September 2010, where there was localized torrential rain, which was recorded as 259.5 mm precipitation in a day at Seoul. The result of the simulation using the FNL as initial and boundary data (FNL exp) showed that the localized heavy rainfall area was not accurately simulated and that the simulated amount of precipitation was about 4% of the observed accumulated precipitation. That of the simulation using KLAPS analyses as initial and boundary data (KLAPC exp) showed that the localized heavy rainfall area was simulated on the northern area of Seoul-Gyeonggi area, which renders rather difference in location, and that the simulated amount was underestimated as about 6.4% of the precipitation. Finally, that of the simulation using an initial field made by assimilating the radar data to the KLAPS using 3DVAR system (KLAP3D exp) showed that the localized heavy rainfall area was located properly on Seoul-Gyeonggi area, but still the amount itself was underestimated as about 29% of the precipitation. Even though KLAP3D exp still showed an underestimation in the precipitation, it showed the best result among them. Even if it is difficult to generalize the effect of data assimilation by one case, this study showed that the radar data assimilation can somewhat improve the accuracy of the simulated precipitation.

Operational implementation of a hybrid ensemble/4D‐Var global data assimilation system at the Met Office

AbstractWe describe the development and testing of the hybrid ensemble/4D‐Var global data assimilation system that was implemented operationally at the Met Office in July 2011, giving an average reduction of RMS errors of just under 1%. The scheme uses the extended control variable technique to implement a hybrid background error covariance that combines the standard climatological covariance with a covariance derived from the 23‐member operational ensemble MOGREPS‐G. Unique features of the Met Office scheme include application of a horizontal ‘anti‐aliasing’ filter to the ensemble error modes, a vertical localization scheme based uniquely on a modification of the climatological stream function covariance, and inflation of the climatological covariance to maintain the analysis fit to observations. Findings during development include a significantly greater impact of the scheme in 3D‐Var than 4D‐Var, a clear positive impact from the combination of the anti‐aliasing filter and vertical localization, and a relatively small sensitivity to full coupling of the ensemble and 4D‐Var systems. Supplementary experiments suggest that the ability of the ensemble to capture coherent ‘Errors of the Day’ is key to the improvements in forecast skill.A particular problem encountered during development was significantly poorer tropical verification scores when measured against own analyses. In contrast, verification against independent (ECMWF) analyses gave scores that were much more consistent with those against observations.

A drought outlook study in Korea

Techniques are proposed for developing a monthly and weekly drought outlook and the drought outlook components are evaluated. A drought index, the surface water supply index (SWSI) was modified and used for the drought outlook. A water balance model (abcd) was successfully calibrated using a regional regression, including monthly and weekly factors, and was used to convert meteorology to hydrology. For the monthly drought outlook, an ensemble technique was applied, both with and without monthly industrial meteorology information (MIMI). For the weekly drought outlook, a deterministic forecasting technique was applied employing the Global Data Assimilation and Prediction System (GDAPS). The methodologies were applied to the Geum River basin in Korea. While only the weekly outlook using the GDAPS has sufficient forecasting capability to suggest it might be useful, the accuracy of the monthly drought outlook is expected to improve as the climate forecast accuracy increases. Editor Z.W. Kundzewicz; Associate editor D. Hughes Citation Kim, Y.-O., Lee, J.-K., and Palmer, R.N., 2012. A drought outlook study in Korea. Hydrological Sciences Journal, 57 (6), 1141–1153.

Bayesian regression model for seasonal forecast of precipitation over Korea

In this paper, we apply three different Bayesian methods to the seasonal forecasting of the precipitation in a region around Korea (32.5°N–42.5°N, 122.5°E-132.5°E). We focus on the precipitation of summer season (June–July–August; JJA) for the period of 1979–2007 using the precipitation produced by the Global Data Assimilation and Prediction System (GDAPS) as predictors. Through cross-validation, we demonstrate improvement for seasonal forecast of precipitation in terms of root mean squared error (RMSE) and linear error in probability space score (LEPS). The proposed methods yield RMSE of 1.09 and LEPS of 0.31 between the predicted and observed precipitations, while the prediction using GDAPS output only produces RMSE of 1.20 and LEPS of 0.33 for CPC Merged Analyzed Precipitation (CMAP) data. For station-measured precipitation data, the RMSE and LEPS of the proposed Bayesian methods are 0.53 and 0.29, while GDAPS output is 0.66 and 0.33, respectively. The methods seem to capture the spatial pattern of the observed precipitation. The Bayesian paradigm incorporates the model uncertainty as an integral part of modeling in a natural way. We provide a probabilistic forecast integrating model uncertainty.

Assessment of the impact of the meteorological meso-scale circulation on air quality in arid subtropical region

In this paper, COSMO numerical weather prediction model is used to simulate land-sea breeze circulation in the north coast of Oman and to analyze the variability of the PBL depth. Typical summer day 1st August 2009 and winter day 1st January 2009 were simulated and verified by surface observations. The COSMO model was initialized using the German global model which runs as 40km resolution and 40 vertical layers. The simulations show differences in the onset, strength, and inland penetration of the sea breeze. The high sea-land thermal contrast in the summer season induces earlier onset and progression of the sea breeze with larger horizontal and vertical extents. The summer and winter inland penetration is simulated around 150 and 65km, respectively. The PBL depth was defined from the verticals profiles of the turbulence kinetic energy (TKE). The simulated PBL height varied at 1,600m in summer and 700m in the winter case. These results are in general in good agreement with the simulations of the National Centers for Environmental Prediction Global Data Assimilation System (NCEP-GDAS) model. The dispersive ability of the atmosphere was tested through the vertical mixing coefficient (VMC) generated by NCEP-GDAS simulations. The VMC during the summer simulated case is approximately four times larger than the respective one during the winter case. Consequently, during the advection of winter sea breeze, a shallow dome less than 100m is shaped and forms an obstacle to the air-pollutant dispersion.

Evaluation of upper tropospheric humidity from NCEP analysis and WRF Model Forecast with Kalpana observation during Indian summer monsoon 2010

AbstractThis paper presents an evaluation of upper tropospheric humidity (UTH) fields from National Center for Environmental Prediction (NCEP) Global Data Assimilation System (GDAS) analysis and Weather Research and Forecasting (WRF) mesoscale model forecast with satellite derived UTH. For this purpose, UTH (weighted average of relative humidity in 200–500 hPa layer) obtained from Indian geostationary satellite Kalpana is compared with the UTH computed from NCEP analysis and WRF model forecast during the summer monsoon, 2010. Kaplana‐derived products are not included in the NCEP analysis: therefore, a comparison of the two independent datasets is promising. This study indicates that UTH derived from different sources (Kalpana UTH, NCEP Analysed and subsequent model forecasted UTH) matches very well over the Indian region. However, there are region‐specific small departures in UTH which increases the model predicted UTH. Copyright © 2012 Royal Meteorological Society

Impacts of Model Resolutions and Initial Conditions on Predictions of the Asian Summer Monsoon by the NCEP Climate Forecast System

Abstract A series of 60-day hindcasts by the Climate Forecast System (CFS) of the National Centers for Environmental Prediction is analyzed to understand the impacts of atmospheric model resolutions and initial conditions on predictions of the Asian summer monsoon. The experiments, for the time period 2002–06 and with 14 ensemble members, are conducted at resolutions of T62, T126, and T254. They are initialized every 5 days from May to August, using the operational global atmospheric data assimilation system and operational global ocean data assimilation. It is found that, in predicting the magnitude and the timing of monsoon rainfall over lands, high model resolutions overall perform better than lower model resolutions. The increase in prediction skills with model resolution is more apparent over South Asia than over Southeast Asia. The largest improvement is seen over the Tibetan Plateau, at least for precipitation. However, the increase in model resolution does not enhance the skill of the predictions over oceans. Overall, model resolution has larger impacts than do the initial conditions on predicting the development of the Asian summer monsoon in the early season. However, higher model resolutions such as T382 may be needed for the CFS to simulate and predict many features of the monsoon more realistically.

Torrential rainfall responses to ice microphysical processes during pre-summer heavy rainfall over southern China

In this study, the effects of key ice microphysical processes on the pre-summer heavy rainfall over southern China during 3–8 June 2008 were investigated. A series of two-dimensional sensitivity cloud-resolving model simulations were forced with zonally uniform vertical velocity, zonal wind, horizontal temperature, and water vapor advection data from the National Centers for Environmental Prediction (NCEP)/Global Data Assimilation System (GDAS). The effects of key ice microphysical processes on the responses of rainfall to large-scale forcing were analyzed by comparing two sensitivity experiments with a control experiment. In one sensitivity experiment, ice crystal radius, associated with depositional growth of snow from cloud ice, was reduced from 100 µm in the control experiment to 50 µm, and in the other sensitivity experiment the efficiency of the growth of graupel from the accretion of snow was reduced to 50% from 100% in the control experiment. The results show that the domain-mean rainfall responses to these ice microphysical processes are stronger during the decay phase than during the onset and mature phases. During the decay phase, the increased mean rain rate resulting from the decrease in ice crystal radius is associated with the enhanced mean local atmospheric drying, the increased mean local hydrometeor loss, and the suppressed mean water vapor divergence. The increased mean rain rate caused by the reduction in accretion efficiency is related to the reduced mean water vapor divergence and the enhanced mean local hydrometeor loss.

AIRS impact on analysis and forecast of an extreme rainfall event (Indus River Valley, Pakistan, 2010) with a global data assimilation and forecast system

A set of data assimilation and forecast experiments is performed with the NASA Global data assimilation and forecast system GEOS‐5, to compare the impact of different approaches toward assimilation of Atmospheric Infrared Sounder (AIRS) data. The impact is first assessed globally on a sample of more than forty forecasts per experiment, through the standard 500 hPa anomaly correlation metrics. Next, the focus is on precipitation analysis and precipitation forecast skill relative to one particular event: an extreme rainfall episode which occurred in late July 2010 in Pakistan, causing massive floods along the Indus River Valley. Results show that, in addition to improving the global forecast skill, the assimilation of quality‐controlled AIRS temperature retrievals obtained under partly cloudy conditions produce better precipitation analyses, and substantially better 7‐day forecasts, than assimilation of clear‐sky radiances. The improvement of precipitation forecast skill up to 7 days is very significant in the tropics, and is caused by an improved representation, attributed to cloudy retrieval assimilation, of two contributing mechanisms: the low‐level moisture advection, and the concentration of moisture over the area in the days preceding the precipitation peak.

Observational bounds on atmospheric heating by aerosol absorption: Radiative signature of transatlantic dust

Aerosols absorb solar radiation thus changing the atmospheric temperature profile but the overall magnitude of this effect is not known. To that end, Saharan dust emissions over the Atlantic Ocean provide an opportunity to examine aerosol‐related heating via satellite imaging. A major difficulty, however, is disentangling a straightforward heating signal caused by the absorbing dust from a meteorological signal, which originates from correlation between dust concentration and air temperature. To tackle the problem, we combine temperature (T) soundings, from the atmospheric infrared sounder (AIRS), with aerosol optical depth (τ) measurements, from the moderate resolution imaging spectroradiometer (MODIS), and data assimilation results from the global data assimilation system (GDAS). We introduce the quantity β(P) ≡ ∂TP/∂τ, the subscript indicating temperature at a given pressure, and study the observed (AIRS) vs. modeled (GDAS) vertical profiles of β(P). Using the vertical as well as horizontal patterns of β(P) and Δβ(P) ≡ βobs. − βmodl., we avoid instrumental and geographic artifacts and extract a remarkably robust radiative heating signal of about 2–4 K within the dust layer. The extracted signal peaks over the mid‐Atlantic Ocean, as a result of competing trends: “memory” of the dust source in the east, and mixing with transparent aerosol in the west.

An assessment of surface soil temperature products from numerical weather prediction models using ground‐based measurements

Surface soil temperature estimates at approximately 0.05 m depth are needed to retrieve soil moisture from the planned Soil Moisture Active Passive (SMAP) L‐band (1.4 GHz) satellite. Numerical weather prediction (NWP) systems as operated by various weather centers produce global estimates of soil temperature. In this study in situ data collected over the state of Oklahoma are used to assess surface (soil) temperature from three NWP systems: (1) the integrated forecast system from the European Center for Medium range Weather Forecasts (ECMWF), (2) the modern‐era retrospective analysis for research and applications (MERRA) from the NASA Global Modeling and Assimilation Office, and (3) the global data assimilation system used by the National Center for Environmental Prediction (NCEP). The results are presented by hour of day with specific attention directed to the SMAP early morning overpass time at around 6 A.M. local time, and the period of 1 April to 1 October 2009. It was found that the NWP systems estimate the 0.05 m soil temperature at this time of day with an overall root mean square error of 1.9 to 2.0 K. It is shown that this error can be reduced to 1.6 to 1.8 K when differences between the modeling and measurement depth are accounted for by synchronizing each NWP set to match the mean phase of the in situ data and adjusting the amplitude in accordance with heat flow principles. These results indicate that with little calibration all products meet the SMAP error budget criteria over Oklahoma.

Description of atmospheric conditions at the Pierre Auger Observatory using the Global Data Assimilation System (GDAS)

Atmospheric conditions at the site of a cosmic ray observatory must be known for reconstructing observed extensive air showers. The Global Data Assimilation System (GDAS) is a global atmospheric model predicated on meteorological measurements and numerical weather predictions. GDAS provides altitude-dependent profiles of the main state variables of the atmosphere like temperature, pressure, and humidity. The original data and their application to the air shower reconstruction of the Pierre Auger Observatory are described. By comparisons with radiosonde and weather station measurements obtained on-site in Malargüe and averaged monthly models, the utility of the GDAS data is shown.