A study of Cloud Vertical Structure and its association with precipitation over Delhi.

2021 ◽  
Author(s):  
Saloni Sharma ◽  
Amit Kumar Mishra
Keyword(s):  
Vertical Structure ◽  
Prediction Models ◽  
Hydrological Cycle ◽  
Weather Prediction ◽  
Single Layer ◽  
Middle Level ◽  
Cloud Base ◽  
Frequency Of Occurrence ◽  
Monthly Average ◽  
Vertical Location

<p>Water in the atmosphere (in vapour, liquid or ice form) act as a fuel for various atmospheric processes through addition/removal of latent heat. Formation of clouds involves all these processes and thus it greatly affects atmospheric dynamics and thermodynamics. It is important to know the vertical location of clouds in atmosphere in order to understand it’s effect on other important atmospheric variables. The interaction of cloud vertical distribution with other meteorological variables is very significant in determining the hydrological cycle of any region. Therefore, in this study we have found out the cloud vertical structure over Delhi and associated it with the precipitation. The cloud top height, base height and cloud thickness along with their vertical location in the atmosphere is known as cloud vertical structure (CVS). The association of CVS with precipitation involving the amount of precipitation contributed by different layers of cloud could be very helpful in weather prediction models. We have used the balloon based measurements to calculate the CVS and for precipitation we have used CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) data. We have done multiple regressions to determine association between Cloud top height, cloud base height and cloud depth with precipitation. We have also related the monthly average of precipitation with monthly frequency of occurrence of single-layer, double-layer and triple-layer clouds. The frequency of occurrence of clouds classified based on their altitude and depth ( i.e., low-level clouds, middle-level clouds, high-level clouds and deep convective clouds) are also correlated with the monthly average precipitation. </p>

scholarly journals Climatology of Cloud Vertical Structures from Long-Term High-Resolution Radiosonde Measurements in Beijing

Atmosphere ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 401
Author(s):  
Qing Zhou ◽  
Yong Zhang ◽  
Shuze Jia ◽  
Junli Jin ◽  
Shanshan Lv ◽  
...  
Keyword(s):  
High Resolution ◽  
Atmospheric Circulation ◽  
Frequency Distribution ◽  
Hydrological Cycle ◽  
Global Radiation ◽  
Circulation Model ◽  
Middle Level ◽  
Radiation Budget ◽  
Cloud Base

Clouds are significant in the global radiation budget, atmospheric circulation, and hydrological cycle. However, knowledge regarding the observed climatology of the cloud vertical structure (CVS) over Beijing is still poor. Based on high-resolution radiosonde observations at Beijing Nanjiao Weather Observatory (BNWO) during the period 2010–2017, the method for identifying CVS depending on height-resolved relative humidity thresholds is improved, and CVS estimation by radiosonde is compared with observations by millimeter-wave cloud radar and ceilometer at the same site. Good consistency is shown between the three instruments. Then, the CVS climatology, including the frequency distribution and seasonal variation, is investigated. Overall, the occurrence frequency (OF) of cloudy cases in Beijing is slightly higher than that of clear-sky cases, and the cloud OF is highest in summer and lowest in winter. Single-layer clouds and middle-level clouds are dominant in Beijing. In addition, the average cloud top height (CTH), cloud base height (CBH), and cloud thickness in Beijing are 6.2 km, 4.0 km, and 2.2 km, respectively, and show the trend of reaching peaks in spring and minimums in winter. In terms of frequency distribution, the CTH basically resides below an altitude of 16 km, and approximately 43% of the CBHs are located at altitudes of 0.5–1.5 km. The cloud OF has only one peak located at altitudes of 4–8 km in spring, whereas it shows a trimodal distribution in other seasons. The height at which the cloud OF reaches its peak is highest in summer and lowest in winter. To the best of our knowledge, the cloud properties analyzed here are the first to elucidate the distribution and temporal variation of the CVS in Beijing from a long-term sounding perspective, and these results will provide a scientific observation basis for improving the atmospheric circulation model, as well as comparisons and verifications for measurements by ground-based remote sensing equipment.

scholarly journals Low-Atmosphere Drifting Balloons: Platforms for Environment Monitoring and Forecast Improvement

2016 ◽  
Vol 97 (9) ◽  
pp. 1583-1599 ◽  
Author(s):  
A. Doerenbecher ◽  
C. Basdevant ◽  
P. Drobinski ◽  
P. Durand ◽  
C. Fesquet ◽  
...  
Keyword(s):  
Field Experiments ◽  
Prediction Models ◽  
Hydrological Cycle ◽  
Weather Prediction ◽  
Observation System ◽  
Convective Systems ◽  
Leading Role ◽  
Space Agency ◽  
In Situ Investigation ◽  
The Mediterranean

Abstract Balloons are one of the key observing platforms for the atmosphere. Radiosounding is the most commonly used technique and provides over a thousand vertical profiles worldwide every day. These data represent an essential cornerstone of data assimilation for numerical weather prediction systems. Although less common (but equally interesting for the in situ investigation of the atmosphere), drifting boundary layer pressurized balloons (BLPBs) offer rare observational skills. These balloons collect meteorological and/or chemical measurements at isopycnal height as they drift in a quasi-Lagrangian way. The BLPB system presented in this paper was developed by the French Space Agency [Centre National d’Études Spatiales (CNES)] and has been used in field experiments focusing on precipitation in Africa [African Monsoon Multiscale Analysis (AMMA)] and the Mediterranean [Hydrological Cycle in the Mediterranean Experiment (HyMeX)] as well as on air pollution in India [Indian Ocean Experiment (INDOEX)] and the Mediterranean [Transport a Longue Distance et Qualite de l’Air dans le bassin Méditerraneen (TRAQA) and Chemistry–Aerosol Mediterranean Experiment (ChArMeX)]. One important advantage of BLPBs is their capability to explore the lowest layers of the atmosphere above the oceans, areas that remain difficult to access. BLPB had a leading role in a complex adaptive observation system for the forecast of severe precipitation events. These balloons collected data in the marine environment of convective systems, which were assimilated in real time to improve the knowledge of the state of the atmosphere in the numerical prediction models of Météo-France.

scholarly journals Cloud vertical structure over a tropical station obtained using long-term high resolution Radiosonde measurements

2018 ◽  
Author(s):  
Nelli Narendra Reddy ◽  
Madineni Venkat Ratnam ◽  
Ghouse Basha ◽  
Varaha Ravikiran
Keyword(s):  
Large Scale ◽  
Vertical Structure ◽  
Lower Layer ◽  
Monsoon Season ◽  
Single Layer ◽  
Cloud Base ◽  
High Level ◽  
Tropical Station ◽  
Layer Cloud

Abstract. Cloud vertical structure, including top and base altitudes, thickness of cloud layers, and the vertical distribution of multi-layer clouds affects the large-scale atmosphere circulation by altering gradients in the total diabatic heating/cooling and latent heat release. In this study, long-term (11 years) observations of high vertical resolution radiosondes are used to obtain the cloud vertical structure over a tropical station, Gadanki (13.5° N, 79.2° E), India. The detected cloud layers are verified with independent observations using cloud particle sensor (CPS) sonde launched from the same station. High-level clouds account for 69.05 %, 58.49 %, 55.5 %, and 58.6 % of all clouds during pre-monsoon, monsoon, post-monsoon, and winter seasons, respectively. The average cloud base (cloud top) altitude for low-level, middle-level, high-level and deep convective clouds are 1.74 km (3.16 km), 3.59 km (5.55 km), 8.79 km (10.49 km), and 1.22 km (11.45 km), respectively. Single-layer, two-layer, and three-layer clouds account for 40.80 %, 30.71 %, and 19.68 % of all cloud configurations, respectively. Multi-layer clouds occurred more frequently during the monsoon with 34.58 %. Maximum cloud top altitude and the cloud thickness occurred during monsoon season for single-layer clouds and the uppermost layer of multiple layer cloud configurations. In multi-layer cloud configurations, diurnal variations in the thickness of upper layer clouds are larger than those of lower layer clouds. Heating/cooling in the troposphere and lower stratosphere due to these clouds layers is also investigated and found peak cooling (peak warming) below (above) the Cold Point Tropopause (CPT) altitude. The magnitude of cooling (warming) increases from single-layer to four or more-layer cloud occurrence. Further, the vertical structure of clouds is also studied with respect to the arrival date of Indian summer monsoon over Gadanki.

scholarly journals 3D cloud products for weather prediction and climate modelling

2003 ◽  
Vol 58 (2) ◽  
pp. 90-98
Author(s):  
G. Seiz ◽  
E. P. Baltsavias ◽  
A. Gruen
Keyword(s):  
Prediction Models ◽  
Weather Prediction ◽  
Stereo Image ◽  
Cloud Base ◽  
Climate Modelling ◽  
Cloud Motion ◽  
Image Pairs ◽  
Along Track ◽  
Synchronous Time ◽  
Numerical Weather Prediction Models

Abstract. In this paper, the possibilities of satellite-based and ground-based stereoscopy of clouds are examined, with the objective to derive cloud top and cloud base heights and motion. These parameters are very important for a better description of clouds for nowcasting and numerical weather prediction models. For the satellite part, images of ATSR2 (on ERS-2) and MISR (on EOS Terra) are used. As stereo image pairs from polar-orbiting satellites are never perfectly synchronous (time delay of some seconds between the image reeeption from the different viewing angles), the height error of the cloud top heights, introduced by the along-track motion component, is corrected with the cloud top winds extracted from Meteosat-6 and -7. For MISR, with nine viewing angles, this height correction is not needed when at least three images from non-symmetric cameras are used; then, it is possible to directly separate the along-track parallax (due to cloud height) from the along-track wind contribution (due to cloud motion). Our new ground-based imager System was operated in eoineidence with an overpass of ERS-2 in October 1999. The ground measurements proved to be an interesting technique to validate satellite-based cloud top height and motion of vertically thin clouds and to additionally detect more detailed cloud features, which is particularly important for aecurate noweasting in mountainous terrain.

scholarly journals Overview of the first HyMeX Special Observation Period over Italy: observations and model results

2014 ◽  
Vol 18 (5) ◽  
pp. 1953-1977 ◽  
Author(s):  
R. Ferretti ◽  
E. Pichelli ◽  
S. Gentile ◽  
I. Maiello ◽  
D. Cimini ◽  
...  
Keyword(s):  
Prediction Models ◽  
Hydrological Cycle ◽  
Initial Conditions ◽  
Weather Prediction ◽  
Central Italy ◽  
Weather Forecast ◽  
Western Mediterranean ◽  
Target Area ◽  
Northeastern Italy ◽  
Observation Period

Abstract. The Special Observation Period (SOP1), part of the HyMeX campaign (Hydrological cycle in the Mediterranean Experiments, 5 September–6 November 2012), was dedicated to heavy precipitation events and flash floods in the western Mediterranean, and three Italian hydro-meteorological monitoring sites were identified: Liguria–Tuscany, northeastern Italy and central Italy. The extraordinary deployment of advanced instrumentation, including instrumented aircrafts, and the use of several different operational weather forecast models, including hydrological models and marine models, allowed an unprecedented monitoring and analysis of high-impact weather events around the Italian hydro-meteorological sites. This activity has seen strong collaboration between the Italian scientific and operational communities. In this paper an overview of the Italian organization during SOP1 is provided, and selected Intensive Observation Periods (IOPs) are described. A significant event for each Italian target area is chosen for this analysis: IOP2 (12–13 September 2012) in northeastern Italy, IOP13 (15–16 October 2012) in central Italy and IOP19 (3–5 November 2012) in Liguria and Tuscany. For each IOP the meteorological characteristics, together with special observations and weather forecasts, are analyzed with the aim of highlighting strengths and weaknesses of the forecast modeling systems, including the hydrological impacts. The usefulness of having different weather forecast operational chains characterized by different numerical weather prediction models and/or different model set up or initial conditions is finally shown for one of the events (IOP19).

scholarly journals Cloud vertical structure over a tropical station obtained using long-term high-resolution radiosonde measurements

2018 ◽  
Vol 18 (16) ◽  
pp. 11709-11727 ◽  
Author(s):  
Nelli Narendra Reddy ◽  
Madineni Venkat Ratnam ◽  
Ghouse Basha ◽  
Varaha Ravikiran
Keyword(s):  
Large Scale ◽  
Vertical Structure ◽  
Lower Layer ◽  
Single Layer ◽  
Cloud Base ◽  
Heating And Cooling ◽  
High Level ◽  
Tropical Station ◽  
Layer Cloud

Abstract. Cloud vertical structure, including top and base altitudes, thickness of cloud layers, and the vertical distribution of multilayer clouds, affects large-scale atmosphere circulation by altering gradients in the total diabatic heating and cooling and latent heat release. In this study, long-term (11 years) observations of high-vertical-resolution radiosondes are used to obtain the cloud vertical structure over a tropical station at Gadanki (13.5∘ N, 79.2∘ E), India. The detected cloud layers are verified with independent observations using cloud particle sensor (CPS) sonde launched from the same station. High-level clouds account for 69.05 %, 58.49 %, 55.5 %, and 58.6 % of all clouds during the pre-monsoon, monsoon, post-monsoon, and winter seasons, respectively. The average cloud base (cloud top) altitudes for low-level, middle-level, high-level, and deep convective clouds are 1.74 km (3.16 km), 3.59 km (5.55 km), 8.79 km (10.49 km), and 1.22 km (11.45 km), respectively. Single-layer, two-layer, and three-layer clouds account for 40.80 %, 30.71 %, and 19.68 % of all cloud configurations, respectively. Multilayer clouds occurred more frequently during the monsoon with 34.58 %. Maximum cloud top altitude and cloud thickness occurred during the monsoon season for single-layer clouds and the uppermost layer of multiple-layer cloud configurations. In multilayer cloud configurations, diurnal variations in the thickness of upper-layer clouds are larger than those of lower-layer clouds. Heating and cooling in the troposphere and lower stratosphere due to these cloud layers are also investigated and peak cooling (peak warming) is found below (above) the cold-point tropopause (CPT) altitude. The magnitude of cooling (warming) increases from single-layer to four- or more-layer cloud occurrence. Further, the vertical structure of clouds is also studied with respect to the arrival date of the Indian summer monsoon over Gadanki.

Stochastic modelling and prediction of monthly surface temperatures: StocSIPS

2020 ◽  
Author(s):  
Lenin Del Rio Amador ◽  
Shaun Lovejoy
Keyword(s):  
Prediction Models ◽  
Weather Prediction ◽  
Computational Cost ◽  
Transition Time ◽  
Approximate Symmetry ◽  
Tropical Ocean ◽  
Low Frequencies ◽  
Monthly Average ◽  
Global Circulation Models ◽  
Del Rio

<p>From hourly to decadal time scales, atmospheric fields are characterized by two scaling regimes: at high frequencies the weather, with fluctuations increasing with the time scale, and at low frequencies, macroweather with fluctuations decreasing with scale, the transition between the two at <em>τ<sub>w</sub></em>. This transition time is the lifetime of planetary structures and is therefore close to the deterministic predictability limit of conventional numerical weather prediction models. While it is thus the outer scale of deterministic weather models, conversely, it is the inner scale of stochastic macroweather models.</p><p>Here we explore the spatial dependence of this transition time. Starting at the surface (2m temperature) we found that the monthly average temperature falls in the macroweather regime for almost any location in the globe, except for parts of the tropical ocean where <em>τ<sub>w </sub></em>∼ 1 - 2 years. As we increase in altitude, the dependence of <em>τ<sub>w</sub></em> with the location becomes more homogeneous and above 850mb <em>τ<sub>w</sub></em> < 1 month almost everywhere. The longer tropical ocean transition scales are presumably the deterministic outer scales of the “ocean weather” regime.</p><p>Knowledge of <em>τ<sub>w</sub></em> is fundamental for stochastic macroweather forecasting.   Such forecasting is based on symmetries, primarily the power-law behavior of the fluctuations that implies a huge memory that can be exploited for forecasts up to several years. In addition, there is another approximate symmetry called “statistical space-time factorization” relating spatial and temporal statistics. Finally, while weather regime temperature fluctuations are highly intermittent, in macroweather the intermittency is much lower, fluctuations are quasi Gaussian.</p><p>The Stochastic Seasonal and Interannual Prediction System (StocSIPS<sup>[1,2]</sup>) is a stochastic data-driven model that exploits these symmetries to perform macroweather (long-term) forecasts. Compared to traditional global circulation models (GCM), it has the advantage of forcing predictions to converge to the real-world climate (not the model climate). It extracts the internal variability (weather noise) directly from past data and does not suffer from model drift. Some other practical advantages include much lower computational cost, no need for downscaling and no ad hoc postprocessing.</p><p>We show that StocSIPS can predict monthly average surface temperature (nearly) to its stochastic predictability limits. Using monthly to annual lead time hindcasts, we compare StocSIPS predictions with those from the CanSIPS<sup>[3]</sup> GCM. Beyond a month, and especially over land, StocSIPS generally has higher skill. For regular StocSIPS forecasts, see http://www.physics.mcgill.ca/StocSIPS/.</p><p><strong>References</strong></p><p><sup>[1]</sup> Del Rio Amador, L. and Lovejoy, S. (2019) Clim Dyn, <strong>53</strong>: 4373. https://doi.org/10.1007/s00382-019-04791-4</p><p><sup>[2]</sup> Lovejoy, S., Del Rio Amador, L., Hébert, R. (2017) In Nonlinear Advances in Geosciences, A.A. Tsonis ed. Springer Nature, 305–355 DOI: 10.1007/978-3-319-58895-7</p><p><sup>[3]</sup> Merryfield WJ, Denis B, Fontecilla JS, Lee WS, Kharin S, Hodgson J, Archambault B (2011) Rep., 51pp, Environment Canada.</p>

Forecasting of Tornadoes and Downbursts

2020 ◽  
Author(s):  
Djordje Romanic
Keyword(s):  
Chaos Theory ◽  
Prediction Models ◽  
Weather Prediction ◽  
Small Scale ◽  
Weather Forecasts ◽  
Wind Speeds ◽  
The Past ◽  
Extreme Wind ◽  
Extreme Wind Speeds ◽  
Numerical Weather Prediction Models

Tornadoes and downbursts cause extreme wind speeds that often present a threat to human safety, structures, and the environment. While the accuracy of weather forecasts has increased manifold over the past several decades, the current numerical weather prediction models are still not capable of explicitly resolving tornadoes and small-scale downbursts in their operational applications. This chapter describes some of the physical (e.g., tornadogenesis and downburst formation), mathematical (e.g., chaos theory), and computational (e.g., grid resolution) challenges that meteorologists currently face in tornado and downburst forecasting.

scholarly journals HGPT2: An ERA5-Based Global Model to Estimate Relative Humidity

2021 ◽  
Vol 13 (11) ◽  
pp. 2179
Author(s):  
Pedro Mateus ◽  
Virgílio B. Mendes ◽  
Sandra M. Plecha
Keyword(s):  
Water Vapor ◽  
Relative Humidity ◽  
Real Time ◽  
Prediction Models ◽  
Weather Prediction ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
Global Navigation Satellite Systems ◽  
International Gnss Service ◽  
Weighted Mean Temperature

The neutral atmospheric delay is one of the major error sources in Space Geodesy techniques such as Global Navigation Satellite Systems (GNSS), and its modeling for high accuracy applications can be challenging. Improving the modeling of the atmospheric delays (hydrostatic and non-hydrostatic) also leads to a more accurate and precise precipitable water vapor estimation (PWV), mostly in real-time applications, where models play an important role, since numerical weather prediction models cannot be used for real-time processing or forecasting. This study developed an improved version of the Hourly Global Pressure and Temperature (HGPT) model, the HGPT2. It is based on 20 years of ERA5 reanalysis data at full spatial (0.25° × 0.25°) and temporal resolution (1-h). Apart from surface air temperature, surface pressure, zenith hydrostatic delay, and weighted mean temperature, the updated model also provides information regarding the relative humidity, zenith non-hydrostatic delay, and precipitable water vapor. The HGPT2 is based on the time-segmentation concept and uses the annual, semi-annual, and quarterly periodicities to calculate the relative humidity anywhere on the Earth’s surface. Data from 282 moisture sensors located close to GNSS stations during 1 year (2020) were used to assess the model coefficients. The HGPT2 meteorological parameters were used to process 35 GNSS sites belonging to the International GNSS Service (IGS) using the GAMIT/GLOBK software package. Results show a decreased root-mean-square error (RMSE) and bias values relative to the most used zenith delay models, with a significant impact on the height component. The HGPT2 was developed to be applied in the most diverse areas that can significantly benefit from an ERA5 full-resolution model.

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