اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدThe Infrared Astronomical Characteristics of Roque de los Muchachos Observatory: precipitable water vapor statistics
119
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
The atmospheric water vapor content above the Roque de los Muchachos Observatory (ORM) obtained from Global Positioning Systems (GPS) is presented. GPS measurements have been evaluated by comparison with 940nm-radiometer observations. Statistical analysis of GPS measurements points to ORM as an observing site with suitable conditions for infrared (IR) observations, with a median column of precipitable water vapor (PWV) of 3.8 mm. PWV presents a clear seasonal behavior, being Winter and Spring the best seasons for IR observations. The percentage of nighttime showing PWV values smaller than 3 mm is over 60% in February, March and April. We have also estimated the temporal variability of water vapor content at the ORM. A summary of PWV statistical results at different astronomical sites is presented, recalling that these values are not directly comparable as a result of the differences in the techniques used to recorded the data.
قيم البحث
اقرأ أيضاً
We validate the Weather Research and Forecasting (WRF) model for precipitable water vapour (PWV) forecasting as a fully operational tool for optimizing astronomical infrared (IR) observations at Roque de los Muchachos Observatory (ORM). For the model
validation we used GNSS-based (Global Navigation Satellite System) data from the PWV monitor located at the ORM. We have run WRF every 24 h for near two months, with a horizon of 48 hours (hourly forecasts), from 2016 January 11 to 2016 March 4. These runs represent 1296 hourly forecast points. The validation is carried out using different approaches: performance as a function of the forecast range, time horizon accuracy, performance as a function of the PWV value, and performance of the operational WRF time series with 24- and 48-hour horizons. Excellent agreement was found between the model forecasts and observations, with R = 0.951 and R = 0.904 for the 24- and 48-h forecast time series respectively. The 48-h forecast was further improved by correcting a time lag of 2 h found in the predictions. The final errors, taking into account all the uncertainties involved, are 1.75 mm for the 24-h forecasts and 1.99 mm for 48 h. We found linear trends in both the correlation and RMSE of the residuals (measurements - forecasts) as a function of the forecast range within the horizons analysed (up to 48 h). In summary, the WRF performance is excellent and accurate, thus allowing it to be implemented as an operational tool at the ORM.
We present the largest database so far of atmospheric optical-turbulence profiles (197035 individual CN2(h)) for an astronomical site, the Roque de los Muchachos Observatory (La Palma, Spain). This C2 (h) database was obtained through generalized-SCI
DAR observations at the 1 meter Jacobus Kapteyn telescope from Febrary 2004 to August 2009, obtaining useful data for 211 nights. The overestimation of the turbulence strength induced during the generalized SCIDAR data processing has been analyzed for the different observational configurations. All the individual C2 (h) have been recalibrated to compensate the introduced errors during data treatment following (Avila & Cuevas 2009). Comparing results from profiles before and after the recalibration, we analyze its impact on the calculation of relevant parameters for adaptive optics.
We report on the measurements of telluric water vapor made with the instrument FIFI-LS on SOFIA. Since November 2018, FIFI-LS has measured the water vapor overburden with the same measurement setup on each science flight with about 10 data points thr
oughout the flight. This created a large sample of 469 measurements at different locations, flight altitudes and seasons. The paper describes the measurement principle in detail and provides some trend analysis on the 3 parameters. This presents the first systematic analysis with SOFIA based on in situ observations.
Long-Short-Term-Memory (LSTM) networks have been used extensively for time series forecasting in recent years due to their ability of learning patterns over different periods of time. In this paper, this ability is applied to learning the pattern of
Global Positioning System (GPS)-based Precipitable Water Vapor (PWV) measurements over a period of 4 hours. The trained model was evaluated on more than 1500 hours of recorded data. It achieves a root mean square error (RMSE) of 0.098 mm for a forecasting interval of 5 minutes in the future, and outperforms the naive approach for a lead-time of up to 40 minutes.
Water masers are good tracers of high-mass star-forming regions. Water maser VLBI observations provide a good probe to study high-mass star formation and the galactic structure. We plan to make a blind survey toward the northern Galactic plane in fut
ure years using 25m radio telescope of Xinjiang Astronomical Observatory. We will select some water maser sources discovered in the survey and make high resolution observations and study the gas kinematics close to the high-mass protostar.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
