scholarly journals Combining TRMM and Surface Observations of Precipitation: Technique and Validation over South America

2010 ◽  
Vol 25 (3) ◽  
pp. 885-894 ◽  
Author(s):  
José Roberto Rozante ◽  
Demerval Soares Moreira ◽  
Luis Gustavo G. de Goncalves ◽  
Daniel A. Vila
Keyword(s):  
Large Scale ◽  
Numerical Models ◽  
Spatial Scales ◽  
Model Performance ◽  
Evaluation Process ◽  
Tropical Rainfall Measuring Mission ◽  
Atmospheric Model ◽  
Continuous Assessment ◽  
Precipitation Estimates ◽  
Surface Observations

Abstract The measure of atmospheric model performance is highly dependent on the quality of the observations used in the evaluation process. In the particular case of operational forecast centers, large-scale datasets must be made available in a timely manner for continuous assessment of model results. Numerical models and surface observations usually work at distinct spatial scales (i.e., areal average in a regular grid versus point measurements), making direct comparison difficult. Alternatively, interpolation methods are employed for mapping observational data to regular grids and vice versa. A new technique (hereafter called MERGE) to combine Tropical Rainfall Measuring Mission (TRMM) satellite precipitation estimates with surface observations over the South American continent is proposed and its performance is evaluated for the 2007 summer and winter seasons. Two different approaches for the evaluation of the performance of this product against observations were tested: a cross-validation subsampling of the entire continent and another subsampling of only areas with sparse observations. Results show that over areas with a high density of observations, the MERGE technique’s performance is equivalent to that of simply averaging the stations within the grid boxes. However, over areas with sparse observations, MERGE shows superior results.

scholarly journals Evaluating the accuracy and uncertainty of atmospheric and wave model hindcasts during severe events using model ensembles

2021 ◽  
Author(s):  
Ali Abdolali ◽  
Andre van der Westhuysen ◽  
Zaizhong Ma ◽  
Avichal Mehra ◽  
Aron Roland ◽  
...  
Keyword(s):  
Extreme Event ◽  
Numerical Models ◽  
Model Performance ◽  
Ground Truth ◽  
Wave Model ◽  
Atmospheric Model ◽  
Stationary Source ◽  
Source Point ◽  
Spectral Wave Model ◽  
Model Ensembles

AbstractVarious uncertainties exist in a hindcast due to the inabilities of numerical models to resolve all the complicated atmosphere-sea interactions, and the lack of certain ground truth observations. Here, a comprehensive analysis of an atmospheric model performance in hindcast mode (Hurricane Weather and Research Forecasting model—HWRF) and its 40 ensembles during severe events is conducted, evaluating the model accuracy and uncertainty for hurricane track parameters, and wind speed collected along satellite altimeter tracks and at stationary source point observations. Subsequently, the downstream spectral wave model WAVEWATCH III is forced by two sets of wind field data, each includes 40 members. The first ones are randomly extracted from original HWRF simulations and the second ones are based on spread of best track parameters. The atmospheric model spread and wave model error along satellite altimeters tracks and at stationary source point observations are estimated. The study on Hurricane Irma reveals that wind and wave observations during this extreme event are within ensemble spreads. While both Models have wide spreads over areas with landmass, maximum uncertainty in the atmospheric model is at hurricane eye in contrast to the wave model.

scholarly journals Process-Based Model Prediction of Coastal Dune Erosion through Parametric Calibration

2021 ◽  
Vol 9 (6) ◽  
pp. 635
Author(s):  
Hyeok Jin ◽  
Kideok Do ◽  
Sungwon Shin ◽  
Daniel Cox
Keyword(s):  
Large Scale ◽  
Numerical Models ◽  
Model Performance ◽  
Coastal Dunes ◽  
Wave Transformation ◽  
Storm Event ◽  
Face Model ◽  
Dune Erosion ◽  
Simulation Performance ◽  
Sand Bar

Coastal dunes are important morphological features for both ecosystems and coastal hazard mitigation. Because understanding and predicting dune erosion phenomena is very important, various numerical models have been developed to improve the accuracy. In the present study, a process-based model (XBeachX) was tested and calibrated to improve the accuracy of the simulation of dune erosion from a storm event by adjusting the coefficients in the model and comparing it with the large-scale experimental data. The breaker slope coefficient was calibrated to predict cross-shore wave transformation more accurately. To improve the prediction of the dune erosion profile, the coefficients related to skewness and asymmetry were adjusted. Moreover, the bermslope coefficient was calibrated to improve the simulation performance of the bermslope near the dune face. Model performance was assessed based on the model-data comparisons. The calibrated XBeachX successfully predicted wave transformation and dune erosion phenomena. In addition, the results obtained from other two similar experiments on dune erosion with the same calibrated set matched well with the observed wave and profile data. However, the prediction of underwater sand bar evolution remains a challenge.

scholarly journals Evaluation of numerical models by FerryBox and fixed platform in situ data in the southern North Sea

Ocean Science ◽  
2015 ◽  
Vol 11 (6) ◽  
pp. 879-896 ◽  
Author(s):  
M. Haller ◽  
F. Janssen ◽  
J. Siddorn ◽  
W. Petersen ◽  
S. Dick
Keyword(s):  
North Sea ◽  
High Frequency ◽  
Numerical Models ◽  
Spatial Scales ◽  
Model Performance ◽  
Eastern Coast ◽  
Southern North Sea ◽  
In Situ Data ◽  
Oyster Ground ◽  
Study Results

Abstract. For understanding and forecasting of hydrodynamics in coastal regions, numerical models have served as an important tool for many years. In order to assess the model performance, we compared simulations to observational data of water temperature and salinity. Observations were available from FerryBox transects in the southern North Sea and, additionally, from a fixed platform of the MARNET network. More detailed analyses have been made at three different stations, located off the English eastern coast, at the Oyster Ground and in the German Bight. FerryBoxes installed on ships of opportunity (SoO) provide high-frequency surface measurements along selected tracks on a regular basis. The results of two operational hydrodynamic models have been evaluated for two different time periods: BSHcmod v4 (January 2009 to April 2012) and FOAM AMM7 NEMO (April 2011 to April 2012). While they adequately simulate temperature, both models underestimate salinity, especially near the coast in the southern North Sea. Statistical errors differ between the two models and between the measured parameters. The root mean square error (RMSE) of water temperatures amounts to 0.72 °C (BSHcmod v4) and 0.44 °C (AMM7), while for salinity the performance of BSHcmod is slightly better (0.68 compared to 1.1). The study results reveal weaknesses in both models, in terms of variability, absolute levels and limited spatial resolution. Simulation of the transition zone between the coasts and the open sea is still a demanding task for operational modelling. Thus, FerryBox data, combined with other observations with differing temporal and spatial scales, can serve as an invaluable tool not only for model evaluation, but also for model optimization by assimilation of such high-frequency observations.

scholarly journals Comparison of generalized non-data-driven lake and reservoir routing models for global-scale hydrologic forecasting of reservoir outflow at diurnal time steps

2020 ◽  
Vol 24 (5) ◽  
pp. 2711-2729 ◽  
Author(s):  
Joseph L. Gutenson ◽  
Ahmad A. Tavakoly ◽  
Mark D. Wahl ◽  
Michael L. Follum
Keyword(s):  
Large Scale ◽  
Spatial Scales ◽  
Model Performance ◽  
Global Scale ◽  
Army Corps ◽  
Time Step ◽  
Large Spatial Scale ◽  
Reservoir Routing ◽  
Routing Methods ◽  
Improved Model

Abstract. Large-scale hydrologic forecasts should account for attenuation through lakes and reservoirs when flow regulation is present. Globally generalized methods for approximating outflow are required but must contend with operational complexity and a dearth of information on dam characteristics at global spatial scales. There is currently no consensus on the best approach for approximating reservoir release rates in large spatial scale hydrologic forecasting, particularly at diurnal time steps. This research compares two parsimonious reservoir routing methods at daily steps: Döll et al. (2003) and Hanasaki et al. (2006). These reservoir routing methods have been previously implemented in large-scale hydrologic modeling applications and have been typically evaluated seasonally. These routing methods are compared across 60 reservoirs operated by the U.S. Army Corps of Engineers. The authors vary empirical coefficients for both reservoir routing methods as part of a sensitivity analysis. The method proposed by Döll et al. (2003) outperformed that presented by Hanasaki et al. (2006) at a daily time step and improved model skill over most run-of-the-river conditions. The temporal resolution of the model influences model performances. The optimal model coefficients varied across the reservoirs in this study and model performance fluctuates between wet years and dry years, and for different configurations such as dams in series. Overall, the method proposed by Döll et al. (2003) could enhance large-scale hydrologic forecasting, but can be subject to instability under certain conditions.

scholarly journals Comparison of Global Precipitation Estimates across a Range of Temporal and Spatial Scales

2016 ◽  
Vol 29 (21) ◽  
pp. 7773-7795 ◽  
Author(s):  
Maria Gehne ◽  
Thomas M. Hamill ◽  
George N. Kiladis ◽  
Kevin E. Trenberth
Keyword(s):  
Time Scales ◽  
Large Scale ◽  
Global Climate ◽  
Spatial Scales ◽  
Rain Gauge ◽  
Random Errors ◽  
Climate Data ◽  
Precipitation Estimates ◽  
Global Precipitation ◽  
Daily Time

Abstract Characteristics of precipitation estimates for rate and amount from three global high-resolution precipitation products (HRPPs), four global climate data records (CDRs), and four reanalyses are compared. All datasets considered have at least daily temporal resolution. Estimates of global precipitation differ widely from one product to the next, with some differences likely due to differing goals in producing the estimates. HRPPs are intended to produce the best snapshot of the precipitation estimate locally. CDRs of precipitation emphasize homogeneity over instantaneous accuracy. Precipitation estimates from global reanalyses are dynamically consistent with the large-scale circulation but tend to compare poorly to rain gauge estimates since they are forecast by the reanalysis system and precipitation is not assimilated. Regional differences among the estimates in the means and variances are as large as the means and variances, respectively. Even with similar monthly totals, precipitation rates vary significantly among the estimates. Temporal correlations among datasets are large at annual and daily time scales, suggesting that compensating bias errors at annual and random errors at daily time scales dominate the differences. However, the signal-to-noise ratio at intermediate (monthly) time scales can be large enough to result in high correlations overall. It is shown that differences on annual time scales and continental regions are around 0.8 mm day−1, which corresponds to 23 W m−2. These wide variations in the estimates, even for global averages, highlight the need for better constrained precipitation products in the future.

scholarly journals Comparison of Hydrologic Model Performance Statistics Using Rain Gauge and NEXRAD Precipitation Input at Different Watershed Spatial Scales and Rainfall Return Frequencies for the Upper St. Johns River, Florida USA

Proceedings ◽  
2018 ◽  
Vol 7 (1) ◽  
pp. 11
Author(s):  
Amanda Bredesen ◽  
Christopher J. Brown
Keyword(s):  
Spatial Scale ◽  
Model Simulation ◽  
Numerical Models ◽  
Spatial Scales ◽  
Model Performance ◽  
Rain Gauge ◽  
Hydrologic Model ◽  
Precipitation Data ◽  
Remotely Sensed Data ◽  
Model Input

Water resources numerical models are dependent upon various input hydrologic field data. As models become increasingly complex and model simulation times expand, it is critical to understand the inherent value in using different input datasets available. One important category of model input is precipitation data. For hydrologic models, the precipitation data inputs are perhaps the most critical. Common precipitation model input includes either rain gauge or remotely-sensed data such next-generation radar-based (NEXRAD) data. NEXRAD data provides a higher level of spatial resolution than point rain gauge coverage, but is subject to more extensive data pre and post processing along with additional computational requirements. This study first documents the development and initial calibration of a HEC-HMS model of a subtropical watershed in the Upper St. Johns River Basin in Florida, USA. Then, the study compares calibration performance of the same HEC-HMS model using either rain gauge or NEXRAD precipitation inputs. The results are further discretized by comparing key calibration statistics such as Nash–Sutcliffe Efficiency for different spatial scale and at different rainfall return frequencies. The study revealed that at larger spatial scale, the calibration performance of the model was about the same for the two different precipitation datasets while the study showed some benefit of NEXRAD for smaller watersheds. Similarly, the study showed that for smaller return frequency precipitation events, NEXRAD data was superior.

scholarly journals Analysis and Reduction of Systematic Errors through a Seamless Approach to Modeling Weather and Climate

2010 ◽  
Vol 23 (22) ◽  
pp. 5933-5957 ◽  
Author(s):  
G. M. Martin ◽  
S. F. Milton ◽  
C. A. Senior ◽  
M. E. Brooks ◽  
S. Ineson ◽  
...  
Keyword(s):  
Time Scales ◽  
Land Surface ◽  
Large Scale ◽  
Climate Models ◽  
Spatial Scales ◽  
Full Range ◽  
Model Development ◽  
Model Performance ◽  
Systematic Errors ◽  
Wide Range

Abstract The reduction of systematic errors is a continuing challenge for model development. Feedbacks and compensating errors in climate models often make finding the source of a systematic error difficult. In this paper, it is shown how model development can benefit from the use of the same model across a range of temporal and spatial scales. Two particular systematic errors are examined: tropical circulation and precipitation distribution, and summer land surface temperature and moisture biases over Northern Hemisphere continental regions. Each of these errors affects the model performance on time scales ranging from a few days to several decades. In both cases, the characteristics of the long-time-scale errors are found to develop during the first few days of simulation, before any large-scale feedbacks have taken place. The ability to compare the model diagnostics from the first few days of a forecast, initialized from a realistic atmospheric state, directly with observations has allowed physical deficiencies in the physical parameterizations to be identified that, when corrected, lead to improvements across the full range of time scales. This study highlights the benefits of a seamless prediction system across a wide range of time scales.

scholarly journals Modelling of orographic precipitation over Iberia: a springtime case study

2010 ◽  
Vol 25 ◽  
pp. 103-110 ◽  
Author(s):  
M. J. Costa ◽  
R. Salgado ◽  
D. Santos ◽  
V. Levizzani ◽  
D. Bortoli ◽  
...  
Keyword(s):  
Iberian Peninsula ◽  
Numerical Models ◽  
Model Performance ◽  
Warm Season ◽  
Atmospheric Model ◽  
Rain Gauge ◽  
Orographic Precipitation ◽  
Mountain Ranges ◽  
Simulated Precipitation

Abstract. Orographic precipitation is a result of very complex processes and its study using numerical models is of utmost importance since it can open an important avenue to the improvement of precipitation forecasts, especially during the warm season. Mainland Portugal is characterised by a very variable terrain between the north and south regions, the latter being much smoother, with sparse mountains that barely reach 1000 m. Conversely, several mountain ranges are distributed over Spain with heights often exceeding 1500 m altitude. A mesoscale non-hydrostatic atmospheric model (MesoNH) is used to study the orographic precipitation during a limited period in spring of 2002 over the Iberian Peninsula. In order to assess the effects of the mountains, case study simulations are done, with and without the orography. MesoNH is initialized and forced by the ECMWF analyses. The effects of orography on precipitation over neighbouring regions are also analyzed. Simulations show that orography is a key factor in determining the spatial distribution of precipitation over the Iberian Peninsula, with enhancements in the regions with mountain ranges and diminution or suppression over certain valleys. The simulated precipitation fields were visually compared with radar observations in central Portugal and quantitatively compared with rain gauge data all over Portugal in order to assess the model performance in the analyzed cases.

scholarly journals Parameterization of the Spatial Variability of Rain for Large-Scale Models and Remote Sensing

2015 ◽  
Vol 54 (10) ◽  
pp. 2027-2046 ◽  
Author(s):  
Z. J. Lebo ◽  
C. R. Williams ◽  
G. Feingold ◽  
V. E. Larson
Keyword(s):  
Spatial Variability ◽  
Large Scale ◽  
Rain Rate ◽  
Numerical Models ◽  
Spatial Scales ◽  
Warm Pool ◽  
Radar Data ◽  
Model Output ◽  
Wide Range ◽  
Averaged Model

AbstractThe spatial variability of rain rate R is evaluated by using both radar observations and cloud-resolving model output, focusing on the Tropical Warm Pool–International Cloud Experiment (TWP-ICE) period. In general, the model-predicted rain-rate probability distributions agree well with those estimated from the radar data across a wide range of spatial scales. The spatial variability in R, which is defined according to the standard deviation of R (for R greater than a predefined threshold Rmin) σ(R), is found to vary according to both the average of R over a given footprint μ(R) and the footprint size or averaging scale Δ. There is good agreement between area-averaged model output and radar data at a height of 2.5 km. The model output at the surface is used to construct a scale-dependent parameterization of σ(R) as a function of μ(R) and Δ that can be readily implemented into large-scale numerical models. The variability in both the rainwater mixing ratio qr and R as a function of height is also explored. From the statistical analysis, a scale- and height-dependent formulation for the spatial variability of both qr and R is provided for the analyzed tropical scenario. Last, it is shown how this parameterization can be used to assist in constraining parameters that are often used to describe the surface rain-rate distribution.

Residual Diagnosis of Diabatic Heating from ERA-40 and NCEP Reanalyses: Intercomparisons with TRMM

2009 ◽  
Vol 22 (2) ◽  
pp. 414-428 ◽  
Author(s):  
Steven C. Chan ◽  
Sumant Nigam
Keyword(s):  
Large Scale ◽  
Diabatic Heating ◽  
Tropical Rainfall Measuring Mission ◽  
Latent Heating ◽  
Maritime Continent ◽  
Precipitation Estimates ◽  
Trmm Precipitation ◽  
Heating Profiles ◽  
The Tropics ◽  
Heating Profile

Abstract Diabatic heating is diagnosed from the 40-yr ECMWF Re-Analysis (ERA-40) circulation as a residue in the thermodynamic equation. The heating distribution is compared with the heating structure diagnosed from NCEP and 15-yr ECMWF Re-Analysis (ERA-15) circulation and latent heating generated from Tropical Rainfall Measuring Mission (TRMM) observations using the convective–stratiform heating (CSH) algorithm. The ERA-40 residual heating in the tropics is found to be stronger than NCEP’s (and ERA-15), especially in July when its zonal–vertical average is twice as large. The bias is strongest over the Maritime Continent in January and over the eastern basins and Africa in July. Comparisons with precipitation indicate ERA-40 heating to be much more realistic over the eastern Pacific but excessive over the Maritime Continent, by at least 20% in January. Intercomparison of precipitation estimates from heating-profile integrals and station and satellite analyses reveals the TRMM CSH latent heating to be chronically weak by as much as a factor of 2! It is the low-side outlier among nine precipitation estimates in three of the four analyzed regions. No less worrisome is the inconsistency between the integral of the CSH latent heating profile in the tropics and the TRMM precipitation retrievals constraining the CSH algorithm (e.g., the 3A25 analysis). Confronting TRMM’s diagnosis of latent heating from local rainfall retrievals and local cumulus-model heating profiles with heating based on the large-scale assimilated circulation is a defining attribute of this study.

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