scholarly journals LONG-RANGE WEATHER PREDICTION III: MINIATURIZED DISTRIBUTED SENSORS FOR GLOBAL ATMOSPHERIC MEASUREMENTS

2002 ◽  
Author(s):  
EDWARD TELLER ◽  
CECIL LEITH ◽  
GREGORY CANAVAN ◽  
LOWELL WOOD
Keyword(s):  
Long Range ◽  
Weather Prediction ◽  
Atmospheric Measurements ◽  
Distributed Sensors

scholarly journals An Evaluation of Radiative Transfer Simulations of Cloudy Scenes from a Numerical Weather Prediction Model at Sub-Millimetre Frequencies Using Airborne Observations

2020 ◽  
Vol 12 (17) ◽  
pp. 2758
Author(s):  
Stuart Fox
Keyword(s):  
Optical Properties ◽  
Radiative Transfer ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Full Range ◽  
Numerical Weather Prediction Model ◽  
Atmospheric Measurements ◽  
Numerical Weather ◽  
Nwp Model ◽  
Cloud Ice

The Ice Cloud Imager (ICI) will be launched on the next generation of EUMETSAT polar-orbiting weather satellites and make passive observations between 183 and 664 GHz which are sensitive to scattering from cloud ice. These observations have the potential to improve weather forecasts through direct assimilation using "all-sky" methods which have been successfully applied to microwave observations up to 200 GHz in current operational systems. This requires sufficiently accurate representations of cloud ice in both numerical weather prediction (NWP) and radiative transfer models. In this study, atmospheric fields from a high-resolution NWP model are used to drive radiative transfer simulations using the Atmospheric Radiative Transfer Simulator (ARTS) and a recently released database of cloud ice optical properties. The simulations are evaluated using measurements between 89 and 874 GHz from five case studies of ice and mixed-phase clouds observed by the Facility for Airborne Atmospheric Measurements (FAAM) BAe-146 research aircraft. The simulations are strongly sensitive to the assumed cloud ice optical properties, but by choosing an appropriate ice crystal model it is possible to simulate realistic brightness temperatures over the full range of sub-millimetre frequencies. This suggests that sub-millimetre observations have the potential to be assimilated into NWP models using the all-sky method.

Validating snow surface radiative transfer models between 89 and 243GHz using airborne observations over Arctic tundra

2020 ◽  
Author(s):  
Kirsty Wivell ◽  
Melody Sandells ◽  
Nick Rutter ◽  
Stuart Fox ◽  
Chawn Harlow ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Weather Prediction ◽  
Arctic Tundra ◽  
Scanning System ◽  
Polar Regions ◽  
Atmospheric Measurements ◽  
Microphysical Properties ◽  
Snow Properties ◽  
Research Aircraft ◽  
Microwave Radiances

<p>Satellite microwave radiances in atmospheric sounding bands, such as the 183GHz water vapour band, are an important source of data for Numerical Weather Prediction. However, these observations are frequently discarded in polar regions as they are also sensitive to the surface, and there is large uncertainty in the background surface emissivity which depends on the microphysical properties of the snowpack. We evaluate simulations of brightness temperature and emissivity from the Snow Microwave Radiative Transfer (SMRT) model for Arctic tundra snow at frequencies between 89 and 243GHz to assess the potential of being able to assimilate observations at key sounding frequencies, such as 183GHz. In-situ measurements of the surface snowpack were collected for 36 snow pits in Trail Valley Creek, near Inuvik, Canada during the March 2018 Measurements of Arctic Cloud, Snow, and Sea Ice nearby the Marginal Ice Zone (MACSSIMIZE) campaign, a collaboration between the Met Office, Northumbria University, Edinburgh University and the Universite de Sherbrooke. These snowpack measurements provide realistic microphysical snow properties as input to SMRT. We present the evaluation of SMRT simulations against surface-based radiometer observations and airborne observations taken with the Microwave Airborne Radiometer Scanning System (MARSS) and International Submillimetre Airborne Radiometer (ISMAR) on the Facility for Airborne Atmospheric Measurements (FAAM) BAe 146 research aircraft.</p>

scholarly journals Long-range transport of terrain-induced turbulence from high-resolution numerical simulations

2011 ◽  
Vol 11 (22) ◽  
pp. 11793-11805 ◽  
Author(s):  
M. Katurji ◽  
S. Zhong ◽  
P. Zawar-Reza
Keyword(s):  
High Resolution ◽  
Aspect Ratio ◽  
Numerical Simulations ◽  
Long Range ◽  
Prediction Models ◽  
Atmospheric Transport ◽  
Weather Prediction ◽  
Measurement Technology ◽  
Topographic Features ◽  
Range Transport

Abstract. Over complex terrain, an important question is how various topographic features may generate or alter wind turbulence and how far the influence can be extended downstream. Current measurement technology limits the capability in providing a long-range snapshot of turbulence as atmospheric eddies travel over terrain, interact with each other, change their productive and dissipative properties, and are then observed tens of kilometers downstream of their source. In this study, we investigate through high-resolution numerical simulations the atmospheric transport of terrain-generated turbulence in an atmosphere that is neutrally stratified. The simulations are two-dimensional with an isotropic spatial resolution of 15 m and run to a quasi-steady state. They are designed in such a way to allow an examination of the effects of a bell-shaped experimental hill with varying height and aspect ratio on turbulence properties generated by another hill 20 km upstream. Averaged fields of the turbulent kinetic energy (TKE) imply that terrain could have a large influence on velocity perturbations at least 30H (H is the terrain height) upstream and downstream of the terrain, with the largest effect happening in the area of the largest pressure perturbations. The results also show that downstream of the terrain the TKE fields are sensitive to the terrain's aspect ratio with larger enhancement in turbulence by higher aspect ratio, while upstream there is a suppression of turbulence that does not appear to be sensitive to the terrain aspect ratio. Instantaneous vorticity fields shows very detailed flow structures that resemble a multitude of eddy scales dynamically interacting while shearing oppositely paired vortices. The knowledge of the turbulence production and modifications by topography from these high-resolution simulations can be helpful in understanding long-range terrain-induced turbulence and improving turbulence parameterizations used in lower resolution weather prediction models.

Correlations versus causality in Stochastic Long-range Forecasting as a Past Value Problem

2021 ◽  
Author(s):  
Lenin Del Rio Amador ◽  
Shaun Lovejoy
Keyword(s):  
Granger Causality ◽  
Long Range ◽  
Power Law ◽  
Weather Prediction ◽  
Emergent Properties ◽  
Relaxation Processes ◽  
Initial Value ◽  
Relaxation Equation ◽  
Cross Correlations

<p>Over time scales between 10 days and 10-20 years – the macroweather regime – atmospheric fields, including the temperature, respect statistical scale symmetries, such as power-law correlations, that imply the existence of a huge memory in the system that can be exploited for long-term forecasts. The Stochastic Seasonal to Interannual Prediction System (StocSIPS) is a stochastic model that exploits these symmetries to perform long-term forecasts. It models the temperature as the high-frequency limit of the (fractional) energy balance equation (fractional Gaussian noise) which governs radiative equilibrium processes when the relevant equilibrium relaxation processes are power law, rather than exponential. They are obtained when the order of the relaxation equation is fractional rather than integer and they are solved as past value problems rather than initial value problems.</p><p>Long-range weather prediction is conventionally an initial value problem that uses the current state of the atmosphere to produce ensemble forecasts. In contrast, StocSIPS predictions for long-memory processes are “past value” problems that use historical data to provide conditional forecasts. Cross-correlations can be used to define teleconnection patterns, and for identifying possible dynamical interactions, but they do not necessarily imply any causation. Using the precise notion of Granger causality, we show that for long-range stochastic temperature forecasts, the cross-correlations are only relevant at the level of the innovations – not temperatures. Extended here to the multivariate case, (m-StocSIPS) produces realistic space-time temperature simulations. Although it has no Granger causality, we are able to reproduce emergent properties including realistic teleconnection networks and El Niño events and indices.</p>

An Evaluation of Some Ultra-Long-Range Weather Predictions

1980 ◽  
Vol 61 (4) ◽  
pp. 321-328
Author(s):  
Edward E. Hindman ◽  
James Spear
Keyword(s):  
Los Angeles ◽  
Long Range ◽  
Weather Prediction ◽  
Prediction Method ◽  
Data Set ◽  
Highly Correlated ◽  
Random Method

Predictions of wet and dry days made with an ultra-long-range weather prediction method for the Los Angeles, St. Louis, and Boston regions for November 1977–April 1978 are compared with the actual weather. The method predicted, on average, 61% of the wet and dry days correctly. A climatologically based random prediction method is developed and is shown to predict for the same data set, on average, 57% of the wet and dry days correctly. The monthly averages of the daily predictions in each category are highly correlated for the two methods. The ultra-long-range method is shown to predict with about the same skill as the random method for the period investigated.

Estimation of the Added Value of the Absolute Calibration of GPS Radio Occultation Data for Numerical Weather Prediction

2015 ◽  
Vol 143 (4) ◽  
pp. 1259-1274 ◽  
Author(s):  
Josep M. Aparicio ◽  
Stéphane Laroche
Keyword(s):  
Bias Correction ◽  
Radio Occultation ◽  
Weather Prediction ◽  
Added Value ◽  
Atmospheric Measurements ◽  
Gps Radio Occultation ◽  
Correction Scheme ◽  
Occultation Data ◽  
The Impact ◽  
Radio Occultation Data

Abstract An analysis of the impact of GPS radio occultation observations on Environment Canada’s global deterministic weather prediction system is presented. Radio occultation data, as any other source of weather observations, have a direct impact on the analyses. Since they are assimilated assuming that they are well calibrated, they also impact the bias correction scheme employed for other data, such as satellite radiances. The authors estimate the relative impact of occultation data obtained from, first, their assimilation as atmospheric measurements and, second, their influence on the bias correction for radiance data. This assessment is performed using several implementations of the thermodynamic relationships involved, and also allowing or blocking this influence to the radiance bias correction scheme. The current implementation of occultation operators at Environment Canada is presented, collecting upgrades that have been detailed elsewhere, such as the equation of state of air and the expression of refractivity. The performance of the system with and without assimilation of occultations is reviewed under conditions representative of current operations. Several denial runs are prepared, withdrawing only the occultation data from the assimilation, but keeping their influence on the radiance bias correction, or assimilating occultations but denying their impact on the bias correction procedure, and a complete denial. It is shown that the impact of occultations on the analysis is significant through both paths—assimilation and radiance bias correction—albeit the first is larger. The authors conclude that the traceability link of the ensemble of occultations has an added value, beyond the value of each datum as an atmospheric measurement.

scholarly journals 4DVAR assimilation of GNSS zenith path delays and precipitable water into a numerical weather prediction model WRF

2019 ◽  
Vol 12 (1) ◽  
pp. 345-361 ◽  
Author(s):  
Witold Rohm ◽  
Jakub Guzikowski ◽  
Karina Wilgan ◽  
Maciej Kryza
Keyword(s):  
Data Assimilation ◽  
Numerical Weather Prediction ◽  
Vertical Profile ◽  
Weather Prediction ◽  
Wrf Model ◽  
Precipitable Water ◽  
Severe Weather ◽  
Atmospheric Measurements ◽  
Numerical Weather ◽  
Gnss Data

Abstract. The GNSS data assimilation is currently widely discussed in the literature with respect to the various applications for meteorology and numerical weather models. Data assimilation combines atmospheric measurements with knowledge of atmospheric behavior as codified in computer models. With this approach, the “best” estimate of current conditions consistent with both information sources is produced. Some approaches also allow assimilating the non-prognostic variables, including remote sensing data from radar or GNSS (global navigation satellite system). These techniques are named variational data assimilation schemes and are based on a minimization of the cost function, which contains the differences between the model state (background) and the observations. The variational assimilation is the first choice for data assimilation in the weather forecast centers, however, current research is consequently looking into use of an iterative, filtering approach such as an extended Kalman filter (EKF). This paper shows the results of assimilation of the GNSS data into numerical weather prediction (NWP) model WRF (Weather Research and Forecasting). The WRF model offers two different variational approaches: 3DVAR and 4DVAR, both available through the WRF data assimilation (WRFDA) package. The WRFDA assimilation procedure was modified to correct for bias and observation errors. We assimilated the zenith total delay (ZTD), precipitable water (PW), radiosonde (RS) and surface synoptic observations (SYNOP) using a 4DVAR assimilation scheme. Three experiments have been performed: (1) assimilation of PW and ZTD for May and June 2013, (2) assimilation of PW alone; PW, with RS and SYNOP; ZTD alone; and finally ZTD, with RS and SYNOP for 5–23 May 2013, and (3) assimilation of PW or ZTD during severe weather events in June 2013. Once the initial conditions were established, the forecast was run for 24 h. The major conclusion of this study is that for all analyzed cases, there are two parameters significantly changed once GNSS data are assimilated in the WRF model using GPSPW operator and these are moisture fields and rain. The GNSS observations improves forecast in the first 24 h, with the strongest impact starting from a 9 h lead time. The relative humidity forecast in a vertical profile after assimilation of ZTD shows an over 20 % decrease of mean error starting from 2.5 km upward. Assimilation of PW alone does not bring such a spectacular improvement. However, combination of PW, SYNOP and radiosonde improves distribution of humidity in the vertical profile by maximum of 12 %. In the three analyzed severe weather cases PW always improved the rain forecast and ZTD always reduced the humidity field bias. Binary rain analysis shows that GNSS parameters have significant impact on the rain forecast in the class above 1 mm h−1.

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