scholarly journals Hydrologic Implications of Different Large-Scale Meteorological Model Forcing Datasets in Mountainous Regions

2014 ◽  
Vol 15 (1) ◽  
pp. 474-488 ◽  
Author(s):  
Naoki Mizukami ◽  
Martyn P. Clark ◽  
Andrew G. Slater ◽  
Levi D. Brekke ◽  
Marketa M. Elsner ◽  
...  
Keyword(s):  
Wind Speed ◽  
Large Scale ◽  
Prediction Models ◽  
Weather Prediction ◽  
High Elevation ◽  
Climate Impact ◽  
Shortwave Radiation ◽  
Radiative Energy ◽  
Mountainous Regions ◽  
Precipitation And Temperature

Abstract Process-based hydrologic models require extensive meteorological forcing data, including data on precipitation, temperature, shortwave and longwave radiation, humidity, surface pressure, and wind speed. Observations of precipitation and temperature are more common than other variables; consequently, radiation, humidity, pressure, and wind speed often must be either estimated using empirical relationships with precipitation and temperature or obtained from numerical weather prediction models. This study examines two climate forcing datasets using different methods to estimate radiative energy fluxes and humidity and investigates the effects of the choice of forcing data on hydrologic simulations over the mountainous upper Colorado River basin (293 472 km2). Comparisons of model simulations forced by two climate datasets illustrate that the methods used to estimate shortwave radiation impact hydrologic states and fluxes, particularly at high elevation (e.g., ~20% difference in runoff above 3000-m elevation), substantially altering the timing of snowmelt and runoff (~20 days difference) and the partitioning of precipitation between evapotranspiration and runoff. The different forcing datasets also exhibit differences in hydrologic sensitivity to interannual temperature at high elevation. The results suggest that the choice of forcing dataset is an important consideration when conducting climate impact assessments and the subsequent applications of these assessments for water resources planning and management.

scholarly journals Progress toward the Application of a Localized Particle Filter for Numerical Weather Prediction

2019 ◽  
Vol 147 (4) ◽  
pp. 1107-1126 ◽  
Author(s):  
Jonathan Poterjoy ◽  
Louis Wicker ◽  
Mark Buehner
Keyword(s):  
Data Assimilation ◽  
Particle Filter ◽  
Numerical Weather Prediction ◽  
Large Scale ◽  
Sequential Monte Carlo ◽  
Prediction Models ◽  
Weather Prediction ◽  
Coupled Systems ◽  
State Variables ◽  
Numerical Weather

Abstract A series of papers published recently by the first author introduce a nonlinear filter that operates effectively as a data assimilation method for large-scale geophysical applications. The method uses sequential Monte Carlo techniques adopted by particle filters, which make no parametric assumptions for the underlying prior and posterior error distributions. The filter also treats the underlying dynamical system as a set of loosely coupled systems to effectively localize the effect observations have on posterior state estimates. This property greatly reduces the number of particles—or ensemble members—required for its implementation. For these reasons, the method is called the local particle filter. The current manuscript summarizes algorithmic advances made to the local particle filter following recent tests performed over a hierarchy of dynamical systems. The revised filter uses modified vector weight calculations and probability mapping techniques from earlier studies, and new strategies for improving filter stability in situations where state variables are observed infrequently with very accurate measurements. Numerical experiments performed on low-dimensional data assimilation problems provide evidence that supports the theoretical benefits of the new improvements. As a proof of concept, the revised particle filter is also tested on a high-dimensional application from a real-time weather forecasting system at the NOAA/National Severe Storms Laboratory (NSSL). The proposed changes have large implications for researchers applying the local particle filter for real applications, such as data assimilation in numerical weather prediction models.

scholarly journals Sensitivity of Cloud-Resolving Simulations of Warm-Season Convection to Cloud Microphysics Parameterizations

2007 ◽  
Vol 135 (8) ◽  
pp. 2854-2868 ◽  
Author(s):  
Changhai Liu ◽  
Mitchell W. Moncrieff
Keyword(s):  
Large Scale ◽  
Prediction Models ◽  
Weather Prediction ◽  
Warm Season ◽  
Cloud Microphysics ◽  
Mixed Phase ◽  
Fine Grid ◽  
Upper Level ◽  
Heating Profiles ◽  
Microphysical Parameterization

Abstract This paper investigates the effects of cloud microphysics parameterizations on simulations of warm-season precipitation at convection-permitting grid spacing. The objective is to assess the sensitivity of summertime convection predictions to the bulk microphysics parameterizations (BMPs) at fine-grid spacings applicable to the next generation of operational numerical weather prediction models. Four microphysical parameterization schemes are compared: simple ice (Dudhia), four-class mixed phase (Reisner et al.), Goddard five-class mixed phase (Tao and Simpson), and five-class mixed phase with graupel (Reisner et al.). The experimentation involves a 7-day episode (3–9 July 2003) of U.S. midsummer convection under moderate large-scale forcing. Overall, the precipitation coherency manifested as eastward-moving organized convection in the lee of the Rockies is insensitive to the choice of the microphysics schemes, and the latent heating profiles are also largely comparable among the BMPs. The upper-level condensate and cloudiness, upper-level radiative cooling/heating, and rainfall spectrum are the most sensitive, whereas the domain-mean rainfall rate and areal coverage display moderate sensitivity. Overall, the three mixed-phase schemes outperform the simple ice scheme, but a general conclusion about the degree of sophistication in the microphysics treatment and the performance is not achievable.

Atmospheric angular momentum forecasts as novel tests of global numerical weather prediction models

1991 ◽  
Vol 334 (1633) ◽  
pp. 55-92 ◽  
Keyword(s):  
Angular Momentum ◽  
Numerical Weather Prediction ◽  
Large Scale ◽  
Prediction Models ◽  
Weather Prediction ◽  
Atmospheric Angular Momentum ◽  
Axial Component ◽  
Practical Applications ◽  
Numerical Weather ◽  
Three Components

The global circulation of the terrestrial atmosphere exhibits fluctuations of considerable amplitude in all three components of its total angular momentum on interannual, seasonal and shorter timescales. The fluctuations must be intimately linked with nonlinear barotropic and baroclinic energetic conversion processes throughout the whole atmosphere and it is advocated that studies of routinely produced determinations of atmospheric angular momentum (AAM) changes be incorporated into systematic diagnostic investigations of large-scale atmospheric flows, AAM fluctuations are generated by dynamical interactions between the atmosphere and the underlying planet. These excite tiny but measurable compensating fluctuations in the rotation vector of the massive solid Earth, thereby ensuring conservation of the angular momentum of the whole system. Forecasts and analyses of changes in AAM from the output of a global numerical weather prediction (GNWP) model constitute a stringent test of the model. Successful forecasts of the axial com ponent of AAM, and hence of irregular non-tidal components of short-term changes in the Earth’s rotation, would find practical applications in various areas of astronomy and geodesy, such as spacecraft navigation. Reported in this paper are the main findings of intercomparisons of analyses and forecasts of changes in all three components of AAM obtained from the operational GNWP models at the United Kingdom Meteorological Office (UKMO) and the European Centre for Medium Range Weather Forecasts (ECMWF), over the period covering the two years 1987 and 1988. Included in the results obtained is the finding that useful forecasts of changes in the axial component of AAM can be made out to 5 days and even slightly longer.

Vertical profiles of wind gust statistics from a regional reanalysis using multivariate extreme value theory

2020 ◽  
Author(s):  
Julian Steinheuer ◽  
Petra Friederichs
Keyword(s):  
Wind Speed ◽  
Land Surface ◽  
Prediction Models ◽  
Weather Prediction ◽  
Value Theory ◽  
Extreme Value ◽  
Horizontal Wind ◽  
Wind Gust ◽  
Post Processing ◽  
Regional Reanalysis

<div>Wind and gust statistics at the hub height of a wind turbine are important parameters for the planning in the renewable energy sector. However, reanalyses based on numerical weather prediction models typically only give estimates for wind gusts at the standard measurement height of 10 m above the land surface. We present here a statistical post-processing that gives a conditional distribution for hourly peak wind speeds as a function of height. The conditioning variables are provided by the regional reanalysis COSMO-REA6. The post-processing is developed on the basis of observations of the peak wind speed in five vertical layers between 10 m and 250 m of the Hamburg Weather Mast. The statistical post-processing is based on a censored generalized extreme value (cGEV) distribution with non-stationary parameters. To select the most meaningful variables we use a least absolute shrinkage and selection operator. The vertical variation of the cGEV parameters is approximated using Legendre polynomials, allowing gust prediction at any desired height within the training range. Furthermore, the Pickands dependence function is used to investigate dependencies between gusts at different heights. The main predictors are the 10 m gust diagnosis, the barotropic and baroclinic modes of absolute horizontal wind speed, the mean absolute horizontal wind in 700 hPa, the surface pressure tendency and the lifted index. Proper scores show improvements of up to 60 %, especially at higher vertical levels when compared to climatology. The post-processing model with a Legendre approximation is able to provide reliable predictions of gust statistics at unobserved intermediate levels. The strength of the dependence between the gusts at different levels is not stationary and strongly modulated by the vertical stability of the atmosphere.</div>

scholarly journals Vertical profiles of wind gust statistics from a regional reanalysis using multivariate extreme value theory

2019 ◽  
Author(s):  
Julian Steinheuer ◽  
Petra Friederichs
Keyword(s):  
Wind Speed ◽  
Land Surface ◽  
Prediction Models ◽  
Weather Prediction ◽  
Value Theory ◽  
Extreme Value ◽  
Horizontal Wind ◽  
Wind Gust ◽  
Post Processing ◽  
Regional Reanalysis

Abstract. Many applications require wind gust estimates at very different atmospheric height levels. For example, the renewable energy sector is interested in wind and gust predictions at the hub height of a wind power plant. However, numerical weather prediction models typically derive estimates for wind gusts at the standard measurement height of 10 m above the land surface only. Here, we present a statistical post-processing to derive a conditional distribution for hourly peak wind speed as a function of height. The conditioning variables are taken from the regional reanalysis COSMO-REA6. The post-processing is trained using peak wind speed observations at five vertical levels between 10 m and 250 m of the Hamburg Weather Mast. The statistical post-processing is based on a censored generalized extreme value (cGEV) distribution with non-stationary parameters. We use a least absolute shrinkage and selection operator to select the most informative variables. Vertical variations of the cGEV parameters are approximated using Legendre polynomials, such that predictions may be derived at any desired vertical height. Further, the Pickands dependence function is used to assess dependencies between gusts at different heights. The most important predictors are the 10 m gust diagnostic, the barotropic and the baroclinic mode of absolute horizontal wind speed, the mean absolute horizontal wind in 700 hPa, the surface pressure tendency, and the lifted index. Proper scores show improvements with respect to climatology of up to 60 % especially at higher vertical levels. The post-processing model with a Legendre approximation is able to provide reliable predictions of gusts statistics at non-observed intermediate levels. The strength of dependency between gusts at different levels is non-stationary and strongly modulated by the vertical stability of the atmosphere.

scholarly journals Vertical profiles of wind gust statistics from a regional reanalysis using multivariate extreme value theory

2020 ◽  
Vol 27 (2) ◽  
pp. 239-252
Author(s):  
Julian Steinheuer ◽  
Petra Friederichs
Keyword(s):  
Wind Speed ◽  
Prediction Models ◽  
Weather Prediction ◽  
Value Theory ◽  
Extreme Value ◽  
Processing Method ◽  
Horizontal Wind ◽  
Wind Gust ◽  
Post Processing ◽  
Regional Reanalysis

Abstract. Many applications require wind gust estimates at very different atmospheric height levels. For example, the renewable energy sector is interested in wind and gust predictions at the hub height of a wind power plant. However, numerical weather prediction models typically only derive estimates for wind gusts at the standard measurement height of 10 m above the land surface. Here, we present a statistical post-processing method to derive a conditional distribution for hourly peak wind speed as a function of height. The conditioning variables are taken from the COSMO-REA6 regional reanalysis. The post-processing method was trained using peak wind speed observations at five vertical levels between 10 and 250 m from the Hamburg Weather Mast. The statistical post-processing method is based on a censored generalized extreme value (cGEV) distribution with non-homogeneous parameters. We use a least absolute shrinkage and selection operator to select the most informative variables. Vertical variations of the cGEV parameters are approximated using Legendre polynomials, such that predictions may be derived at any desired vertical height. Further, the Pickands dependence function is used to assess dependencies between gusts at different heights. The most important predictors are the 10 m gust diagnostic, the barotropic and the baroclinic mode of absolute horizontal wind speed, the mean absolute horizontal wind at 700 hPa, the surface pressure tendency, and the lifted index. Proper scores show improvements of up to 60 % with respect to climatology, especially at higher vertical levels. The post-processing model with a Legendre approximation is able to provide reliable predictions of gusts' statistics at non-observed intermediate levels. The strength of dependency between gusts at different levels is non-homogeneous and strongly modulated by the vertical stability of the atmosphere.

scholarly journals Alternative Formulations for Incorporating Lateral Boundary Data into Limited-Area Models

2010 ◽  
Vol 138 (7) ◽  
pp. 2867-2882 ◽  
Author(s):  
Martina Tudor ◽  
Piet Termonia
Keyword(s):  
Large Scale ◽  
Prediction Models ◽  
Weather Prediction ◽  
Scale Model ◽  
Coupling Scheme ◽  
Lateral Boundary Condition ◽  
Limited Area ◽  
Lateral Boundary ◽  
Coupling Interval ◽  
Temporal Interpolation

Abstract Limited-area models (LAMs) use higher resolutions and more advanced parameterizations of physical processes than global numerical weather prediction models, but suffer from one additional source of error—the lateral boundary condition (LBC). The large-scale model passes the information on its fields to the LAM only over the narrow coupling zone at discrete times separated by a coupling interval of several hours. The LBC temporal resolution can be lower than the time necessary for a particular meteorological feature to cross the boundary. A LAM user who depends on LBC data acquired from an independent prior analysis or parent model run can find that usual schemes for temporal interpolation of large-scale data provide LBC data of inadequate quality. The problem of a quickly moving depression that is not recognized by the operationally used gridpoint coupling scheme is examined using a simple one-dimensional model. A spectral method for nesting a LAM in a larger-scale model is implemented and tested. Results for a traditional flow-relaxation scheme combined with temporal interpolation in spectral space are also presented.

scholarly journals Parametrizing Horizontally-Averaged Wind and Temperature Profiles in the Urban Roughness Sublayer

2019 ◽  
Vol 173 (3) ◽  
pp. 321-348 ◽  
Author(s):  
Natalie E. Theeuwes ◽  
Reinder J. Ronda ◽  
Ian N. Harman ◽  
Andreas Christen ◽  
C. Sue B. Grimmond
Keyword(s):  
Wind Speed ◽  
Numerical Weather Prediction ◽  
Prediction Models ◽  
Weather Prediction ◽  
Roughness Sublayer ◽  
Vertical Profiles ◽  
Flux Boundary Condition ◽  
Numerical Weather ◽  
Urban Roughness Sublayer ◽  
Urban Roughness

Abstract Tower-based measurements from within and above the urban canopy in two cities are used to evaluate several existing approaches that parametrize the vertical profiles of wind speed and temperature within the urban roughness sublayer (RSL). It is shown that current use of Monin–Obukhov similarity theory (MOST) in numerical weather prediction models can be improved upon by using RSL corrections when modelling the vertical profiles of wind speed and friction velocity in the urban RSL using MOST. Using anisotropic building morphological information improves the agreement between observed and parametrized profiles of wind speed and momentum fluxes for selected methods. The largest improvement is found when using dynamically-varying aerodynamic roughness length and displacement height. Adding a RSL correction to MOST, however, does not improve the parametrization of the vertical profiles of temperature and heat fluxes. This is expected since sources and sinks of heat are assumed uniformly distributed through a simple flux boundary condition in all RSL formulations, yet are highly patchy and anisotropic in a real urban context. Our results can be used to inform the choice of surface-layer representations for air quality, dispersion, and numerical weather prediction applications in the urban environment.

scholarly journals Grappling with uncertainties in physical climate impact projections of water resources

2020 ◽  
Vol 163 (3) ◽  
pp. 1379-1397 ◽  
Author(s):  
Rutger Dankers ◽  
Zbigniew W. Kundzewicz
Keyword(s):  
Large Scale ◽  
Weather Prediction ◽  
Global Scale ◽  
Climate Impact ◽  
Climate Modelling ◽  
Large Uncertainty ◽  
Sources Of Uncertainty ◽  
Hydrological Extremes ◽  
Model Ensembles

AbstractThis paper reviews the sources of uncertainty in physical climate impact assessments. It draws on examples from related fields such as climate modelling and numerical weather prediction in discussing how to interpret the results of multi-model ensembles and the role of model evaluation. Using large-scale, multi-model simulations of hydrological extremes as an example, we demonstrate how large uncertainty at the local scale does not preclude more robust conclusions at the global scale. Finally, some recommendations are made: climate impact studies should be clear about the questions they want to address, transparent about the uncertainties involved, and honest about the assumptions being made.

Tomographic Approach For Tropospheric Water Vapor Detection

2008 ◽  
Vol 8 (3) ◽  
pp. 263-278 ◽  
Author(s):  
P. MIIDLA ◽  
K. RANNAT ◽  
P. UBA
Keyword(s):  
Water Vapor ◽  
Large Scale ◽  
Prediction Models ◽  
Weather Prediction ◽  
Geographical Location ◽  
Vapor Detection ◽  
Gps Receivers ◽  
Cost Efficient ◽  
Effective Network ◽  
Navigation Signals

AbstractThe technical successes in radio navigation and the availability of nu-merical algorithms have promoted the implementation of GPS-technology to atmo-spheric sciences. The tomographical contribution of Global Satellite Navigation Sys-tems (GNSS) is possible due to the methods of high precision detection of tropospheric delays of navigation signals from satellites to receivers. The principal specific char-acter in initial constraints, data collection and assimilation methods, the obtaining of final numerical results and their interpretation make the continuation of the success story for GPS-tomography very challenging. The authors use numerical simulation as the most time- and cost-efficient way to study different processes related to tro-pospheric water vapor tomography. This paper tends to give a short overview about some known methods in GPS-tomography for detection, monitoring and modeling of the tropospheric water vapor. The possible mathematical approach to the construc-tion of virtual network of ground-based sensors (GPS-receivers) for a real geographical location and discretization of the troposphere, also some aspects of raw data filtering and analysis are described. Output of tomographical modelling of the troposphere can be used to improve the results of large-scale numerical weather prediction models and also real-time navigation. The questions of voxel geometry and methods of data processing are supposed to be the key questions in constructing an effective network of GPS-receivers for water vapor tomography.

Sign in / Sign up

Export Citation Format

Share Document