Satellite Link-Budget Statistical Prediction From Weather Forecast Models: Verification With Hayabusa-2 Ka-Band Data

Author(s):  
M. Biscarini ◽  
A. Vittimberga ◽  
K. De Sanctis ◽  
S. Di Fabio ◽  
L. Milani ◽  
...  
2021 ◽  
Author(s):  
Andreas Behrendt ◽  
Florian Spaeth ◽  
Volker Wulfmeyer

<p>We will present recent measurements made with the water vapor differential absorption lidar (DIAL) of University of Hohenheim (UHOH). This scanning system has been developed in recent years for the investigation of atmospheric turbulence and land-atmosphere feedback processes.</p><p>The lidar is housed in a mobile trailer and participated in recent years in a number of national and international field campaigns. We will present examples of vertical pointing and scanning measurements, especially close to the canopy. The water vapor gradients in the surface layer are related to the latent heat flux. Thus, with such low-elevation scans, the latent heat flux distribution over different surface characteristics can be monitored, which is important to verify and improve both numerical weather forecast models and climate models.</p><p>The transmitter of the UHOH DIAL consists of a diode-pumped Nd:YAG laser which pumps a Ti:sapphire laser. The output power of this laser is up to 10 W. Two injection seeders are used to switch pulse-to-pulse between the online and offline signals. These signals are then either directly sent into the atmosphere or coupled into a fiber and guided to a transmitting telescope which is attached to the scanner unit. The receiving telescope has a primary mirror with a dimeter of 80 cm. The backscatter signals are recorded shot to shot and are typically averaged over 0.1 to 1 s.</p>


2019 ◽  
Vol 100 (4) ◽  
pp. 605-619 ◽  
Author(s):  
A. J. Illingworth ◽  
D. Cimini ◽  
A. Haefele ◽  
M. Haeffelin ◽  
M. Hervo ◽  
...  

Abstract To realize the promise of improved predictions of hazardous weather such as flash floods, wind storms, fog, and poor air quality from high-resolution mesoscale models, the forecast models must be initialized with an accurate representation of the current state of the atmosphere, but the lowest few kilometers are hardly accessible by satellite, especially in dynamically active conditions. We report on recent European developments in the exploitation of existing ground-based profiling instruments so that they are networked and able to send data in real time to forecast centers. The three classes of instruments are i) automatic lidars and ceilometers providing backscatter profiles of clouds, aerosols, dust, fog, and volcanic ash, the last two being especially important for air traffic control; ii) Doppler wind lidars deriving profiles of wind, turbulence, wind shear, wind gusts, and low-level jets; and iii) microwave radiometers estimating profiles of temperature and humidity in nearly all weather conditions. The project includes collaboration from 22 European countries and 15 European national weather services, which involves the implementation of common operating procedures, instrument calibrations, data formats, and retrieval algorithms. Currently, data from 265 ceilometers in 19 countries are being distributed in near–real time to national weather forecast centers; this should soon rise to many hundreds. One wind lidar is currently delivering real time data rising to 5 by the end of 2019, and the plan is to incorporate radiometers in 2020. Initial data assimilation tests indicate a positive impact of the new data.


2010 ◽  
Vol 25 (4) ◽  
pp. 1211-1218 ◽  
Author(s):  
Robert R. Gillies ◽  
Shih-Yu Wang ◽  
Jin-Ho Yoon ◽  
Scott Weaver

Abstract A recent study by Gillies and others of persistent inversion events in the Intermountain West of the United States found a substantive linkage between the intraseasonal oscillation (ISO) and the development of persistent inversion events. Given that NCEP’s Climate Forecast System (CFS) has demonstrated skill in the prediction of the ISO as far out as 1 month, it was decided to examine the CFS forecast’s capability in the prediction of such winter persistent inversions. After initial analysis, a simple regression scheme is proposed that is coupled to the CFS output of geopotential height as a way to predict the occurrence of persistent inversion events for Salt Lake City, Utah. Analysis of the CFS hindcasts through the period 1981–2008 indicates that the regression coupled with the CFS can predict persistent inversion events with lead times of up to 4 weeks. The adoption of this coupled regression–CFS prediction may improve the forecasting of persistent inversion events in the Intermountain West, which is currently restricted to the more limited time span (∼10 days) of medium-range weather forecast models.


2018 ◽  
Vol 176 ◽  
pp. 02008
Author(s):  
Erland Källén

The ADM/Aeolus wind lidar mission will provide a global coverage of atmospheric wind profiles. Atmospheric wind observations are required for initiating weather forecast models and for predicting and monitoring long term climate change. Improved knowledge of the global wind field is widely recognised as fundamental to advancing the understanding and prediction of weather and climate. In particular over tropical areas there is a need for better wind data leading to improved medium range (3-10 days) weather forecasts over the whole globe.


2016 ◽  
Vol 5 (4) ◽  
pp. 126 ◽  
Author(s):  
I MADE DWI UDAYANA PUTRA ◽  
G. K. GANDHIADI ◽  
LUH PUTU IDA HARINI

Weather information has an important role in human life in various fields, such as agriculture, marine, and aviation. The accurate weather forecasts are needed in order to improve the performance of various fields. In this study, use artificial neural network method with backpropagation learning algorithm to create a model of weather forecasting in the area of ??South Bali. The aim of this study is to determine the effect of the number of neurons in the hidden layer and to determine the level of accuracy of the method of artificial neural network with backpropagation learning algorithm in weather forecast models. Weather forecast models in this study use input of the factors that influence the weather, namely air temperature, dew point, wind speed, visibility, and barometric pressure.The results of testing the network with a different number of neurons in the hidden layer of artificial neural network method with backpropagation learning algorithms show that the increase in the number of neurons in the hidden layer is not directly proportional to the value of the accuracy of the weather forecasts, the increase in the number of neurons in the hidden layer does not necessarily increase or decrease value accuracy of weather forecasts we obtain the best accuracy rate of 51.6129% on a network model with three neurons in the hidden layer.


ICT Express ◽  
2017 ◽  
Vol 3 (3) ◽  
pp. 124-127 ◽  
Author(s):  
G. Berretta ◽  
P. Dvorak ◽  
M. Luglio ◽  
L. Luini ◽  
C. Riva ◽  
...  

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