Application of Ground based Microwave Radiometer in Aviation Weather Forecasting in Indian Air Force

Time and intensity specific very short-term forecasting or nowcasting is the biggest challenge faced by an Aviation Meteorologist. Ground-based Microwave Radiometer (MWR) has been used for nowcasting convective activity and it was established that there is a good comparison between thermodynamic parameters derived from MWR and GPS radiosonde observations, indicating that MWR observations can be used to develop techniques for nowcasting severe convective activity. In this study, efforts have been made to bring out the efficacy of MWR in nowcasting thunderstorms and fog. Firstly, the observations of MWR located at Palam, New Delhi, India have been compared with the nearest radiosonde (RS) data to ascertain the variation in respective profiles. Large differences were found in Relative Humidity (RH) whereas temperatures from MWR were found to be close to RS observed temperature upto 3.5 Km. Subsequently, the scattered plots and correlation coefficient of thermodynamic indices / parameters indicated that most of the parameters are either not correlated or have moderate correlation only for 1200 UTC profiles. The superepoch technique of lagged composite for various thermodynamic indices / parameters to obtain a combined picture of all the thunderstorm and dense fog cases on the time series could not determine any pattern to predict thunderstorm and dense fog with lead time of 2-4 hours. MWR profile for a case of occurrence of thunderstorm was analyzed. No significant variation was observed in most of the indices (as calculated from MWR observed parameters) prior to the occurrence of thunderstorm. RH at freezing level and between 950 and 700 hPa levels were the only parameters which increased four hours prior to the occurrence.

Download Full-text

Evaluation of WRF Model Forecasts and Their Use for Hydroclimate Monitoring over Southern South America

Weather and Forecasting ◽

10.1175/waf-d-15-0130.1 ◽

2016 ◽

Vol 31 (3) ◽

pp. 1001-1017 ◽

Cited By ~ 3

Author(s):

Omar V. Müller ◽

Miguel A. Lovino ◽

Ernesto H. Berbery

Keyword(s):

South America ◽

Lead Time ◽

Weather Forecasting ◽

Wrf Model ◽

Probability Of Detection ◽

Southern South America ◽

Climate Conditions ◽

Forecast Lead Time ◽

Highly Correlated ◽

Precipitation Season

Abstract Weather forecasting and monitoring systems based on regional models are becoming increasingly relevant for decision support in agriculture and water management. This work evaluates the predictive and monitoring capabilities of a system based on WRF Model simulations at 15-km grid spacing over the La Plata basin (LPB) in southern South America, where agriculture and water resources are essential. The model’s skill up to a lead time of 7 days is evaluated with daily precipitation and 2-m temperature in situ observations for the 2-yr period from 1 August 2012 to 31 July 2014. Results show high prediction performance with 7-day lead time throughout the domain and particularly over LPB, where about 70% of rain and no-rain days are correctly predicted. Also, the probability of detection of rain days is above 80% in humid regions. Temperature observations and forecasts are highly correlated (r > 0.80) while mean absolute errors, even at the maximum lead time, remain below 2.7°C for minimum and mean temperatures and below 3.7°C for maximum temperatures. The usefulness of WRF products for hydroclimate monitoring was tested for an unprecedented drought in southern Brazil and for a slightly above normal precipitation season in northeastern Argentina. In both cases the model products reproduce the observed precipitation conditions with consistent impacts on soil moisture, evapotranspiration, and runoff. This evaluation validates the model’s usefulness for forecasting weather up to 1 week in advance and for monitoring climate conditions in real time. The scores suggest that the forecast lead time can be extended into a second week, while bias correction methods can reduce some of the systematic errors.

Download Full-text

Hurricane Kinetic Energy Spectra from In Situ Aircraft Observations

Journal of the Atmospheric Sciences ◽

10.1175/jas-d-17-0270.1 ◽

2018 ◽

Vol 75 (8) ◽

pp. 2523-2532 ◽

Cited By ~ 3

Author(s):

P. Trent Vonich ◽

Gregory J. Hakim

Keyword(s):

Air Force ◽

Weather Forecasting ◽

Power Spectra ◽

Spectral Slope ◽

Cyclone Intensity ◽

Storm Intensity ◽

Air Force Reserve ◽

Aircraft Data ◽

Made In

Abstract Since the pioneering paper by Nastrom and Gage on aircraft-derived power spectra, significant progress has been made in understanding the wavenumber distribution of energy in Earth’s atmosphere and its implications for the intrinsic limits of weather forecasting. Improvements in tropical cyclone intensity predictions have lagged those of global weather forecasting, and limited intrinsic predictability may be partially responsible. In this study, we construct power spectra from aircraft data of over 1200 missions carried out by the National Oceanic and Atmospheric Administration (NOAA) and Air Force Reserve Command (AFRC) Hurricane Hunters. Each mission is parsed into distinct flight legs, and legs meeting a specified set of criteria are used for spectral analysis. Here, we produce power spectra composites for each category of the Saffir–Simpson scale, revealing a systematic relationship between spectral slope and storm intensity. Specifically, as storm intensity increases, we find that 1) spectral slope becomes steeper across scales from 10 to 160 km and 2) the transition zone where spectral slope begins to steepen shifts downscale.

Download Full-text

Assessments of GMI-Derived Precipitable Water Vapor Products over the South and East China Seas Using Radiosonde and GNSS

Advances in Meteorology ◽

10.1155/2018/7161328 ◽

2018 ◽

Vol 2018 ◽

pp. 1-12 ◽

Cited By ~ 4

Author(s):

Biyan Chen ◽

Wujiao Dai ◽

Zhizhao Liu ◽

Lixin Wu ◽

Pengfei Xia

Keyword(s):

Water Vapor ◽

Weather Forecasting ◽

Microwave Radiometer ◽

Precipitable Water ◽

Weather Conditions ◽

Precipitable Water Vapor ◽

East China ◽

China Seas ◽

The South ◽

Mean Square Errors

Satellite remote sensing of the atmospheric water vapor distribution over the oceans is essential for both weather and climate studies. Satellite onboard microwave radiometer is capable of measuring the water vapor over the oceans under all weather conditions. This study assessed the accuracies of precipitable water vapor (PWV) products over the south and east China seas derived from the Global Precipitation Measurement Microwave Imager (GMI), using radiosonde and GNSS (Global Navigation Satellite System) located at islands and coasts as truth. PWV measurements from 14 radiosonde and 5 GNSS stations over the period of 2014–2017 were included in the assessments. Results show that the GMI 3-day composites have an accuracy of better than 5 mm. A further evaluation shows that RMS (root mean square) errors of the GMI 3-day composites vary greatly in the range of 3∼14 mm at different radiosonde/GNSS sites. GMI 3-day composites show very good agreements with radiosonde and GNSS measured PWVs with correlation coefficients of 0.896 and 0.970, respectively. The application of GMI products demonstrates that it is possible to reveal the weather front, moisture advection, transportation, and convergence during the Meiyu rainfall. This work indicates that the GMI PWV products can contribute to various studies such as climate change, hydrologic cycle, and weather forecasting.

Download Full-text

Technical Note: Intermittent reduction of the stratospheric ozone over Northern Europe caused by a storm in Atlantic Ocean

10.5194/acp-2019-356 ◽

2019 ◽

Author(s):

Mikhail Sofiev ◽

Rostislav Kouznetsov ◽

Risto Hänninen ◽

Viktoria F. Sofieva

Keyword(s):

Weather Forecasting ◽

Stratospheric Ozone ◽

Weather Forecast ◽

Technical Note ◽

Atmospheric Composition ◽

Integrated Modelling ◽

Northern Europe ◽

Ozone Monitoring Instrument ◽

Ozone Concentrations ◽

Short Term Forecasting

Abstract. A three-day episode of anomalously low ozone concentrations in the stratosphere over Northern Europe occurred on 3–5 November 2018. A reduction of the total ozone column down to ~ 200–210 Dobson Units was predicted by the global forecasts of System for Integrated modeLling of Atmospheric coMposition (SILAM) driven by the weather forecast of Integrated Forecasting System (IFS) of European Centre for Medium-Range Weather Forecasting (ECMWF). The reduction down to 210–215 DU was subsequently observed by the satellite instruments, such as Ozone Monitoring Instrument (OMI) and Ozone Mapping Profile Suite (OMPS). The episode was caused by intrusion of the tropospheric air, which was initially uplifted by a storm in Northern Atlantic, south-east of Greenland. Subsequent transport towards the east and further uplift over Scandinavian ridge of this humid and low-ozone air brought it to ~ 25 km altitude causing ~ 30 % reduction of the ozone layer thickness over Northern Europe. The low-ozone air was further transported eastwards and diluted over Siberia, so that the ozone concentrations restored a few days later. High accuracy of the episode prediction 5 days in advance by the IFS-SILAM modelling tandem illustrates the model capabilities of short-term forecasting of the stratospheric composition, including such rare events.

Download Full-text

The benefit of pre- and postprocessing streamflow forecasts for 119 Norwegian catchments, evaluated within the frame of an operational flood-forecasting system

10.5194/egusphere-egu2020-5036 ◽

2020 ◽

Author(s):

Trine Jahr Hegdahl ◽

Kolbjørn Engeland ◽

Ingelin Steinsland ◽

Andrew Singleton

Keyword(s):

Lead Time ◽

Bayesian Model Averaging ◽

Weather Forecasting ◽

False Alarms ◽

Flood Events ◽

Ensemble Forecasts ◽

Flood Warning ◽

Temperature And Precipitation ◽

Streamflow Forecasts ◽

Forecasting System

In this work the performance of different pre- and postprocessing methods and schemes for ensemble forecasts were compared for a flood warning system.&#160; The ECMWF ensemble forecasts of temperature (T) and precipitation (P) were used to force the operational hydrological HBV model, and we estimated 2 years (2014 and 2015) of daily retrospect streamflow forecasts for 119 Norwegian catchments. Two approaches were used to preprocess the temperature and precipitation forecasts: 1) the preprocessing provided by the operational weather forecasting service, that includes a quantile mapping method for temperature and a zero-adjusted gamma distribution for precipitation, applied to the gridded forecasts, 2)&#160; Bayesian model averaging (BMA) applied to the catchment average values of temperature and precipitation. For the postprocessing of catchment streamflow forecasts, BMA was used. Streamflow forecasts were generated for fourteen schemes with different combinations of the raw, pre- and postprocessing approaches for the two-year period for lead-time 1-9 days.The forecasts were evaluated for two datasets: i) all streamflow and ii) flood events. The median flood represents the lowest flood warning level in Norway, and all streamflow observations above median flood are included in the flood event evaluation dataset. We used the continuous ranked probability score (CRPS) to evaluate the pre- and postprocessing schemes. Evaluation based on all streamflow data showed that postprocessing improved the forecasts only up to a lead-time of 2 days, while preprocessing T and P using BMA improved the forecasts for 50% - 90% of the catchments beyond 2 days lead-time. However, with respect to flood events, no clear pattern was found, although the preprocessing of P and T gave better CRPS to marginally more catchments compared to the other schemes.In an operational forecasting system, warnings are issued when forecasts exceed defined thresholds, and confidence in warnings depends on the hit and false alarm ratio. By analyzing the hit ratio adjusted for false alarms, we found that many of the forecasts seemed to perform equally well. Further, we found that there were large differences in the ability to issue correct warning levels between spring and autumn floods. There was almost no ability to predict autumn floods beyond 2 days, whereas the spring floods had predictability up to 9 days for many events and catchments.The results underline differences in the predictability of floods depending on season and the flood generating processes, i.e. snowmelt affected spring floods versus rain induced autumn floods. The results moreover indicate that the ensemble forecasts are less good at predicting correct autumn precipitation, and more emphasis could be put on finding a better method to optimize autumn flood predictions. To summarize we find that the flood forecasts will benefit from pre-/postprocessing, the optimal processing approaches do, however, depend on region, catchment and season.

Download Full-text

Subseasonal Forecast of Surface Air Temperature Using Superensemble Approaches: Experiments over Northeast Asia for 2018

Weather and Forecasting ◽

10.1175/waf-d-20-0096.1 ◽

2021 ◽

Vol 36 (1) ◽

pp. 39-51

Author(s):

Shoupeng Zhu ◽

Xiefei Zhi ◽

Fei Ge ◽

Yi Fan ◽

Ling Zhang ◽

...

Keyword(s):

Air Temperature ◽

Lead Time ◽

Weather Forecasting ◽

Heat Waves ◽

Northeast Asia ◽

Surface Air Temperature ◽

Poor Quality ◽

Spatiotemporal Patterns ◽

Lead Times ◽

Ensemble Mean

AbstractBridging the gap between weather forecasting and climate prediction, subseasonal to seasonal (S2S) forecasts are of great importance yet currently of relatively poor quality. Using the S2S Prediction Project database, the study evaluates products derived from four operational centers of CMA, KMA, NCEP, and UKMO, and superensemble experiments including the straightforward ensemble mean (EMN), bias-removed ensemble mean (BREM), error-based superensemble (ESUP), and Kalman filter superensemble (KF), in forecasts of surface air temperature with lead times of 6–30 days over northeast Asia in 2018. Validations after the preprocessing of a 5-day running mean suggest that the KMA model shows the highest skill for either the control run or the ensemble mean. The nonequal weighted ESUP is slightly superior to BREM, whereas they both show larger biases than EMN after a lead time of 22 days. The KF forecast constantly outperforms the others, decreasing mean absolute errors by 0.2°–0.5°C relative to EMN. Forecast experiments of the 2018 northeast Asia heat wave reveal that the superensembles remarkably improve the raw forecasts featuring biases of >4°C. The prominent advancement of KF is further confirmed, showing the regionally averaged bias of ≤2°C and the hit rate of 2°C reaching up to 60% at a lead time of 22 days. The superensemble techniques, particularly the KF method of dynamically adjusting the weights in accordance with the latest information available, are capable of improving forecasts of spatiotemporal patterns of surface air temperature on the subseasonal time scale, which could extend the skillful prediction lead time of extreme events such as heat waves to about 3 weeks.

Download Full-text

A comparison between TOPEX microwave radiometer, ERS 1 microwave radiometer, and European Centre for Medium-Range Weather Forecasting derived wet tropospheric corrections

Journal of Geophysical Research Atmospheres ◽

10.1029/94jc01104 ◽

1994 ◽

Vol 99 (C12) ◽

pp. 24927 ◽

Cited By ~ 22

Author(s):

Jacques Stum

Keyword(s):

Weather Forecasting ◽

Microwave Radiometer ◽

European Centre ◽

Medium Range ◽

Tropospheric Corrections

Download Full-text

Micro-climatic study and trend analysis of fog characteristics at IGI airport New Delhi using hourly data (1981-2005)

MAUSAM ◽

10.54302/mausam.v63i2.1391 ◽

2021 ◽

Vol 63 (2) ◽

pp. 203-218

Author(s):

RAJENDRA KUMAR JENAMANI

Keyword(s):

Information System ◽

Trend Analysis ◽

Winter Season ◽

New Delhi ◽

Drastic Reduction ◽

Fog Forecasting ◽

Hourly Data ◽

The World ◽

Dense Fog ◽

To Come

Indira Gandhi International (IGI) airport, New Delhi where near about 675 flights on an averagedepart and arrive daily, is highly susceptible to dense fog occurrences during the winter season. In the present paper, anattempt has been made for development of an intensity based fog climatological information system for December andJanuary based on hourly visibility data of 25-years (1981-2005) recorded at IGI airport. Variations and trends if any werealso analyzed along with their extreme years and dates of occurrences. Data since 1964 were also used to find climaticjumps in the trend which includes various higher visibilities of no fog conditions. Besides various vital fog climatologicalinformation generated through the present study for use in aviation, the most important finding is the alarming increasingtrend of the dense fog (< 200m) occurrences in both the months up to as high as 10-20 times from 1960s in contrast tounusual drastic reduction of higher visibility hours to as low as one thirtieth to one fiftieth of hours which were observedin 1960s. Thus, finally making IGI airport, a unique airport in the world which hardly experiences good visibilityconditions (>5000m) in both the months. By considering the unexpected huge annual growth of 30% in both air trafficand passengers that India including IGI has presently been experiencing against the global average of 6%, such visibilitytrend also confirms that present flight disruptions and passengers sufferings in winter will be aggravated more severely indays to come unless CAT-III ILS implemented fully. Finally, we have computed further number of consecutive hours,spell periodicity, most favorable climatological timing of fog onset and fog dispersal based on various intensities for usein aviation and fog forecasting.

Download Full-text

ON A NEW APPROACH TO FORECASTING TORNADO-DANGEROUS SITUATIONS AND OTHER DANGEROUS CONVECTIVE PHENOMENA

Journal of Oceanological Research ◽

10.29006/1564-2291.jor-2019.47(1).42 ◽

2019 ◽

Vol 47 (1) ◽

pp. 135-137

Author(s):

B.Ya. Shmerlin ◽

M.A. Novitskii ◽

O.V. Kalmikova

Keyword(s):

Velocity Field ◽

Vertical Velocity ◽

Lead Time ◽

Weather Forecasting ◽

Wrf Model ◽

Threshold Value ◽

Convective Cell ◽

Point Of View ◽

Convective System ◽

New Approach

In many studies indices of convective instability (hereinafter simply indices) are used to analyze and predict tornado-dangerous situations. For calculating the meteorological fields from which indices were subsequently calculated, the WRF-ARW version 3.4 was used – the non-hydrostatic, regional weather forecasting system. In the works (Novitskii et al, 2016; 2018) as an example of the calculation of 10 tornadoes that occurred at different times in the European territory of the Russian Federation, we show that the most informative from the point of view of forecasting tornado-dangerous situations and providing a minimum number of false warnings is the STP (significant tornado parameter) index. The characteristic time, during which STP index exceeds threshold value, is within the order of an hour, the size of the regions of localization of the values of the indices above the threshold is within the order of several tens kilometers. We proposed along with the STP index to involve the vertical velocity field, calculated in the WRF model, in the analysis and forecast of tornado-dangerous situations. We show that the value of the STP index above the threshold leads within the WRF model to the formation of a localized intense convective cell in the vertical velocity field in the vicinity of the maximum value of the index and at the moment of reaching this value. The possibility of using the STP index to predict tornado-dangerous situations with a lead time of up to three days with an accuracy of 150 km in space and several hours in time is demonstrated. A new approach to short-term forecasting of tornadoes is proposed. It is based on calculating the fields that are visible on the radar screen, using the WRF model forecast. Such fields are the fields of maximum radar reflectivity, upper cloud boundary and integral vertical water content. The comparison of the prognostic fields with the real fields that the radar sees allows us to specify a real convective system at the time of its formation in which the STP index will subsequently reach a threshold value and a tornado will appear. This can enlarge a lead time of tornado warnings to several hours, which currently averages 13 minutes. The approach can also be used for forecasting other dangerous convective phenomena, as well as in any other forecast models for current forecast correction by using incoming radar (satellite) information.

Download Full-text