numerical weather model
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2021 ◽  
Author(s):  
Gregor Köcher ◽  
Tobias Zinner ◽  
Christoph Knote ◽  
Eleni Tetoni ◽  
Florian Ewald ◽  
...  

Abstract. The representation of cloud microphysical processes contributes substantially to the uncertainty of numerical weather simulations. In part, this is owed to some fundamental knowledge gaps in the underlying processes due to the difficulty to observe them directly. On the path to close these gaps we present a setup for the systematic characterization of differences between numerical weather model and radar observations for convective weather situations. Radar observations are introduced which provide targeted dual-wavelength and polarimetric measurements of convective clouds with the potential to provide more detailed information about hydrometeor shapes and sizes. A convection permitting regional weather model setup is established using 5 different microphysics schemes (double-moment, spectral bin (FSBM), and particle property prediction (P3)). Observations are compared to hindcasts which are created with a polarimetric radar forward simulator for all measurement days. A cell-tracking algorithm applied to radar and model data facilitates comparison on a cell object basis. Statistical comparisons of radar observations and numerical weather model runs are presented on a dataset of 30 convection days. In general, simulations show too few weak and small-scale convective cells. Contoured frequency by altitude distributions of radar signatures reveal deviations between the schemes and observations in ice and liquid phase. Apart from the P3 scheme, simulated reflectivities in the ice phase are too high. Dual-wavelength signatures demonstrate issues of most schemes to correctly represent ice particle size distributions, producing overly large graupel particles. Comparison of polarimetric radar signatures reveal issues of all schemes except the FSBM to correctly represent rain particle size distributions.


2021 ◽  
Vol 8 (1) ◽  
pp. 36
Author(s):  
Shallys Alfonso Aguila ◽  
Adrian Fuentes Barrios ◽  
Maibys Sierra Lorenzo

The evaluation of the Nowcasting and very short-range prediction system of the National Meteorological Service of Cuba is presented. The WRF numerical weather model is the primary tool employed in the system. The assessment is done for the relative humidity, precipitation, temperature, wind, and pressure during 2019 and for the simulation domain of highest spatial resolution (3 km). The measurements of the meteorological surface stations were used in the analysis. As result, the system has good ability to forecast the aforementioned variables, and its behavior is better in the pressure and temperature fields, while the worst results were obtained for precipitation. Although there was not much difference between the four initialization (0000, 0600, 1200, and 1800 UTC), the initialization at 1200 UTC stood out among the others because, in general, it had better performance in the forecast of the variables studied.


2021 ◽  
Author(s):  
Chaiyaporn Kitpracha ◽  
Robert Heinkelmann ◽  
Markus Ramatschi ◽  
Kyriakos Balidakis ◽  
Benjamin Männel ◽  
...  

Abstract. Atmospheric ties are theoretically affected by the height differences between antennas at the same site and the meteorological conditions. However, there is often a discrepancy between the expected zenith delay differences and those estimated from geodetic analysis, potentially degrading a combined solution employing atmospheric ties. In order to investigate the possible effects on GNSS atmospheric delay, this study set up an experiment of four co-located GNSS stations of the same type, both antenna and receiver. Specific height differences for each antenna w.r.t the reference antenna are given. One antenna was equipped with a radome at the same height and type as a antenna close to the ground. In addition, a meteorological sensor was used for meteorological data recording. The results show that tropospheric ties from the analytical equation based on meteorological data from GPT3, Numerical Weather Model, and in-situ measurements, and ray-traced tropospheric ties, reduced the bias of zenith delay roughly by 72 %. However, the in-situ tropospheric ties yield the best precision in this study. These results demonstrate, that the instrument effects on GNSS zenith delays were mitigated by using the same instrument. In contrast, the radome causes unexpected bias of GNSS zenith delays in this study. Additionally, multipath effects at low-elevation observations degraded the tropospheric east gradients.


2021 ◽  
Vol 5 (1) ◽  
pp. 41-50
Author(s):  
Deffi Munadiyat Putri ◽  
◽  
Aries Kristianto ◽  

Flood is one of the most common hydro-meteorological disasters. Bengawan Solo is one of the watersheds in Indonesia that also hit by this disaster. This study discusses the flood disaster in the Bengawan Solo area in early March 2019. The purpose of this study is to conduct a discharge simulation using numerical weather model Global Forecast System (GFS) data through Integrated Flood Analysis System (IFAS) so it is possible to predict discharge in the future. There are three types of numerical weather model GFS data that have been downscale using weather research and forecasting model which differentiated based on spin-up time. The numerical weather model product is then used as rainfall data input for IFAS simulation. Based on the analysis, the flood discharge simulation using an 84-hour spin-up time has a satisfactory performance in describing the change in discharge with respect to time. This happens because numerical weather models will be better at quantifying processes that occur on a meso scale with spatial scale of 10 to 1000 km. The result of this research shows that it is possible to predict river discharge up to 84 hours before the disaster so this is can support the mitigation process for hydrometeorological disasters.


2021 ◽  
Author(s):  
Natalia Hanna ◽  
Estera Trzcina ◽  
Maciej Kryza ◽  
Witold Rohm

<p>The numerical weather model starts from the initial state of the Earth's atmosphere in a given place and time. The initial state is created by blending the previous forecast runs (first-guess), together with observations from different platforms. The better the initial state, the better the forecast; hence, it is worthy to combine new observation types. The GNSS tomography technique, developed in recent years, provides a 3-D field of humidity in the troposphere. This technique shows positive results in the monitoring of severe weather events. However, to assimilate the tomographic outputs to the numerical weather model, the proper observation operator needs to be built.</p><p>This study demonstrates the TOMOREF operator dedicated to the assimilation of the GNSS tomography‐derived 3‐D fields of wet refractivity in a Weather Research and Forecasting (WRF) Data Assimilation (DA) system. The new tool has been tested based on wet refractivity fields derived during a very intense precipitation event. The results were validated using radiosonde observations, synoptic data, ERA5 reanalysis, and radar data. In the presented experiment, a positive impact of the GNSS tomography data assimilation on the forecast of relative humidity (RH) was noticed (an improvement of root‐mean‐square error up to 0.5%). Moreover, within 1 hour after assimilation, the GNSS data reduced the bias of precipitation up to 0.1 mm. Additionally, the assimilation of GNSS tomography data had more influence on the WRF model than the Zenith Total Delay (ZTD) observations, which confirms the potential of the GNSS tomography data for weather forecasting.</p>


2020 ◽  
Vol 13 (1) ◽  
pp. 59
Author(s):  
Joshua Hrisko ◽  
Prathap Ramamurthy ◽  
David Melecio-Vázquez ◽  
Jorge E. Gonzalez

Heat storage, ΔQs, is quantified for 10 major U.S. cities using a method called the thermal variability scheme (TVS), which incorporates urban thermal mass parameters and the variability of land surface temperatures. The remotely sensed land surface temperature (LST) is retrieved from the GOES-16 satellite and is used in conjunction with high spatial resolution land cover and imperviousness classes. New York City is first used as a testing ground to compare the satellite-derived heat storage model to two other methods: a surface energy balance (SEB) residual derived from numerical weather model fluxes, and a residual calculated from ground-based eddy covariance flux tower measurements. The satellite determination of ΔQs was found to fall between the residual method predicted by both the numerical weather model and the surface flux stations. The GOES-16 LST was then downscaled to 1-km using the WRF surface temperature output, which resulted in a higher spatial representation of storage heat in cities. The subsequent model was used to predict the total heat stored across 10 major urban areas across the contiguous United States for August 2019. The analysis presents a positive correlation between population density and heat storage, where higher density cities such as New York and Chicago have a higher capacity to store heat when compared to lower density cities such as Houston or Dallas. Application of the TVS ultimately has the potential to improve closure of the urban surface energy balance.


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