Meteorological Observations Research Articles

Wind direction and speed calculation model with deep learning

Abstract In view of the problems of complex process and weak representation in regular atmospheric wind field detection, this paper adopts deep learning method, uses global high-altitude meteorological detection data, and establishes a deep learning model for calculating wind direction and speed with different altitudes and temperatures by using keras software package. The model is verified by using third-party independent sounding data from a meteorological observatory in Shanghai. The calculation accuracy of the model above 2000 m is 0.9830 and the value of loss function is 0.0482. The accuracy under 2000 m is 0.9164 and the value of loss function is 0.0377. There are significant differences in the performance of the model between under 2000 meters and above 2000 meters due to surface friction. The model shows that wind direction and speed of different height layers can be calculated by using only height and temperature at the same height. This model can also be used to check whether the quality of regular wind detection work is good or not with big old data.

Analysis of the thermal control system for the 3U CubeSat nanosatellite – “IXAYA”

Abstract In recent years, space exploration has become increasingly accessible to a diverse array of countries, facilitated in part by CubeSat nanosatellites. These versatile nanosatellites find applications in a wide spectrum of fields, from forest fire detection and meteorological observation to communication systems, particle analysis, and hot spot identification. This article delves into the outcomes of a comprehensive thermal analysis conducted on the IXAYA nanosatellite. The primary mission of this nanosatellite is to identify hot spots in the vicinity of Mexico City. The thermal analysis process entails a meticulous review of each subsystem along with the associated boundary conditions. This study commences with the definition of an optical payload and a thorough assessment of subsystem requirements. Subsequently, we calculate both external and internal thermal loads. With these foundational data points, we progress to ascertain the temperature distribution across the entirety of the satellite, ensuring that all constituent elements remain within their designated operational temperature ranges. In the closing section, we present our findings and conclusions.

Nonlinear Effects of Agricultural Technology on Sustainable Grain Production in China

Grain production is an important element of the United Nations Sustainable Development Goals, regarding livelihoods and social stability. This article uses data on agricultural technology, social factor and grain production in China from 2011 to 2022, and uses the Generalized Additive Model (GAM) to deeply explore the nonlinear impact of agricultural technology and social factor on grain production. The results of the study show that (1) China’s grain output is generally on a growing trend, but the growth rate is declining and fluctuating significantly. There is a significant difference in grain production before and after the COVID-19 epidemic. Moreover, the output in the northern region is significantly higher than that in the south. (2) Except for Consumption expenditure per capita, all other agricultural technology and social factor variables are positively correlated with grain out. (3) The impact of agricultural technology and social factor on grain output shows significant non-linear characteristics, and its impact effect varies in different intervals. Specifically, When the value of the agricultural meteorological observation service station is 20-25, the effective irrigation area is greater than 1800, consumption expenditure per capita greater than 17000 and the total sowing area of crops is 7500, it can significantly increase grain yield. On the contrary, if the emission value of chemical oxygen demand exceeds 130, it has a significant inhibitory effect on grain yield. Furthermore, the effect on grain yield peaks when the total power of agricultural machinery, GDP, and the number of unemployed people in cities approach 3000, 10000, and 20, respectively. The results of the study provide an important basis for optimizing the allocation of agricultural resources and enhancing the efficiency of grain production. Finally, some practical policy recommendations are put forward.

Red-Wattled Lapwing (Vanellus indicus): A Traditional Rain Forecaster for Tribal Farmers of Southwestern Rajasthan

Abstract An intensive survey was carried out over the past 8 years in tribal-dominated areas of southwestern Rajasthan to document red-wattled lapwing (Vanellus indicus) behavior as a rain forecaster, relevant to farm planning and better crop production. The present study included tribal farmers, agriculture scientists, extension workers, ornithologists, and farm workers. The precision of their observations was compared with a contemporaneous study of 10–15 red-wattled lapwing nests across the 46-ha operational area of the Agricultural Research Sub-Station (ARSS) and Adaptive Trial Center (ATC), Sumerpur, Pali, Rajasthan (25.1526°N latitude, 73.0823°E longitude). Local knowledge was gathered through surveys and focus group discussions with the respondents to discuss lapwing behavior as a rain prediction indicator. Meteorological data were gathered from the ATC farm observatory. The findings revealed that the older tribal farmers have an assortment of knowledge about the behavior of red-wattled lapwing as rain prediction indicators such as nesting sites, egg position, egg arrangement, nesting material, numbers of eggs, and egg-laying patterns. These predictions have a direct correlation with modern meteorological observations. It was also noted that the elder tribal farmers planned their farms using lapwing indicators in order to increase crop production, whereas younger generations overlook the traditional rain prediction indicators and have significant knowledge about meteorological data-based weather forecasting. Both the elder and younger tribal farmers were equally interested in meteorological weather forecast announcements, but due to network connectivity challenges, residents in some remote places were unable to access modern meteorological information. Long-term accurate rain forecasting is needed for better agricultural planning and to secure crop production. Rain forecasting can be improved by blending modern meteorological information with traditional proven indications. Farmers who have considerable traditional knowledge along with modern technological updates are capable of using both sources of information in suitable and successful farm planning for better crop production. Significance Statement The purpose of this study is to examine red-wattled lapwing behavior as rain measurements and compare its reliability with modern rain forecasting methods. We also develop a theoretical framework for understanding traditional and modern rain forecasting methods for disaster management in remote and accessible areas, to assist resource management and to minimize costs of cultivation. Our results provide a guideline for blending both types of knowledge and for providing relevant information to remote tribal areas at appropriate times for better farm planning and crop production.

Revealing the influence of wind vortex shedding on the stressed-strained state of steel tower structures with solid cross-section

This work investigated the effect of vortex shedding during the action of wind on tower structures with a solid cross-section. Failure to take into account this influence, together with the manifestation of the phenomena of physical wear of structures during long-term operation, can lead to building accidents. The object of the research are tower structures with a solid cross-section, which are widely used in modern infrastructure ‒ advertising pylons with a transparent advertising structure with a height of about 12, 22, 25 m, and a flagpole with a height of 48 m. The most frequent manifestations of oscillations according to the first natural frequency and shape have been considered, which occur even with moderate winds and when a large number of oscillation cycles occur. In this work, the number of oscillating cycles per year was determined and estimated based on the archive of meteorological observations. It was found that the number of oscillating cycles due to the action of wind excitation for the example of the structures under study is from 2.6 to 14.4 million per year, which requires the mandatory limitation of stresses in the parts of structures during the design to enable their durability. The magnitude of forces from vortex shedding for the studied structures ranges from 2.9 to 43.5 % of the forces caused by the influence of the frontal wind, depending on the height of the structure. Thus, it was found that the influence of vortex shedding is very insignificant for structures up to 12–15 m high and increases for structures 20 m and higher. Rational forms of the cross-sections of structures were established to reduce the influence of vortex shedding ‒ these are cross-sections of a circular and a cross-section close to it. In a general formulation, these are sections for which peak stresses relative to forces in one plane fall into the neutral zone for stresses from forces in another plane. Recommendations have been also given on the simultaneous consideration of forces from the action of frontal wind on the structure and vortex shedding since both manifestations of wind action on tower structures cannot be separated

Innovative approach of meteorological observation integration to develop state-of-the-art web based visualization tool

Innovative approach of meteorological observation integration to develop state-of-the-art web based visualization tool

Nonlinear and Non‐Gaussian Ensemble Assimilation of MOPITT CO

AbstractSatellite retrievals of carbon monoxide (CO) are routinely assimilated in atmospheric chemistry models to improve air quality forecasts, produce reanalyzes and to estimate emissions. This study applies the quantile‐conserving ensemble filter framework, a novel assimilation algorithm that can deal with non‐Gaussian and modestly nonlinear distributions. Instead of assuming normal distributions like the Ensemble Adjustments Kalman Filter (EAKF), we now apply a bounded normal rank histogram (BNRH) distribution for the prior. The goal is to efficiently estimate bounded quantities such as CO atmospheric mixing ratios and emission fluxes while maintaining the good performance achieved by the EAKF. We contrast assimilating meteorological and MOPITT (Measurement of Pollution in the Troposphere) observations for May 2018. We evaluate the results with the fourth deployment of the NASA Atmospheric Tomography Mission (ATom‐4) airborne field campaign. We also compare simulations with CO tropospheric columns from the network for the detection of atmospheric composition change and surface in‐situ observations from NOAA carbon cycle greenhouse gases. While the differences remain small, the BNRH approach clearly works better than the EAKF in comparison to all observation data sets.

Characteristics of surface ozone pollution and its correlations with meteorological factors: the case of Xiangtan.

Based on ozone (O3) monitoring data for Xiangtan and meteorological observation data for 2020-2022, we examined ozone pollution characteristics and the effects of meteorological factors on daily maximum 8-h average ozone (O3-8h) concentrations in Xiangtan. Thus, we observed significant increases as well as notable seasonal variations in O3-8h concentrations in Xiangtan during the period considered. The ozone and temperature change response slope (KO3-T) indicated that local emissions had no significant effect on O3-8h generation. Further, average O3-8h concentration and maximum temperature (Tmax) values showed a polynomial distribution. Specifically, at Tmax < 27 °C, it increased almost linearly with increasing temperature, and at Tmax between 27 and 37 °C, it showed an upward curvilinear trend as temperature increased, but at a much lower rate. Then, at Tmax > 37 °C, it decreased with increasing temperature. With respect to relative humidity (RH), the average O3-8h concentration primarily exceeded the standard value when RH varied in the range of 45-65%, which is the key humidity range for O3 pollution, and the inflection point for the correlation curve between O3-8h concentration and RH appeared at ~55%. Furthermore, at wind speeds (WSs) below 1.5 m∙s-1, O3-8h concentration increased rapidly, and at WSs in the 1.5-2 m∙s-1 range, it increased at a much faster rate. However, at WSs > 2 m∙s-1, it decreased slowly with increasing WS. O3-8h concentration also showed the tendency to exceed the standard value when the dominant wind directions in Xiangtan were easterly or southeasterly.

Evaluation of the Planetary Boundary Layer Height From ERA5 Reanalysis With MOSAiC Observations Over the Arctic Ocean

AbstractThe planetary boundary layer height (PBLH) is a crucial indicator reflecting the region of the atmosphere characterized by continuous turbulence. Here, we use radiosonde and surface meteorological observations (4–7 times per day, year‐round measurements) during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition to derive the PBLH (PBLHMOSAiC), and further evaluate the PBLH from the ERA5 reanalysis (PBLHERA5). Comparisons between PBLHMOSAiC and PBLHERA5 from different perspectives reveal that: (a) The overestimation of PBLHERA5 when the sea ice concentration is &gt;90% is significant with the centered root mean squared error reaching up to 201 m; (b) The difference between the two products is notably pronounced in cold seasons, while it is comparatively diminished in warm seasons; (c) In neutral boundary layers, differences in PBLHERA5 are larger compared with stable and convective boundary layers. In addition, the analysis of error sources indicates that the bias of PBLHERA5 is sensitive to the bias of vertical thermal structure and wind speed profiles in ERA5 data sets in all conditions. Finally, we find a Random Forest model effectively reduces the bias of PBLHERA5 with the index of agreement reaching up to 0.71 in the test data set, while a multiple linear regression demonstrates comparable performance to the Random Forest model.

Cloud platform-based design of automatic monitoring and alarm system for integrated meteorological observation

This study designs an automatic monitoring and alarm system for integrated meteorological observation based on the cloud platform to improve the automatic monitoring ability for integrated meteorological observation and the operation and maintenance management level of meteorological departments. A meteorological data collector was used to collect comprehensive meteorological observation data, transmit the collected comprehensive meteorological observation data to the main control processing module, preprocess the comprehensive meteorological observation data, and finally transmit the pre-processed comprehensive meteorological observation data to the cloud service module through the data transmission module. In the cloud service module, cloud computing and an improved K-means algorithm were used to analyze the comprehensive meteorological observation data, mine abnormal comprehensive meteorological observation data, and send alarm information to the application module. Users can view the comprehensive meteorological observation data in the cloud server on the client in real-time, thus realizing automatic monitoring and alarm for comprehensive meteorological observation. Results show that the average transmission rate of various meteorological comprehensive observation data transmitted by the system is as high as 99.48%, thereby achieving clustering of abnormal meteorological comprehensive observation data. Upon detecting abnormal meteorological data, the system sends alarm information to the mobile phone client to complete the monitoring of abnormal meteorological information.

Comparing annual extreme winds in Iran predicted by numerical weather forecasting and Gram-Charlier statistical model with meteorological observation data

Iran has been experiencing severe dust storms due to strong wind speeds that cause sand erosion. This erosion is particularly pronounced in the semi-arid and arid zones. Wind speed predictions using numerical weather forecasting (NWF) are indispensable; however, the applicability of NWF for predicting extreme annual wind speeds is not well known. To validate the NWF model, this study compared NWF-model-predicted 3-hour averaged wind speeds over one year and those observed at 390 weather stations. In addition, a statistical model was employed to estimate the probability densities of wind speeds for both the observational data and the NWF output. As an NWF model, the mesoscale Weather Research and Forecasting (WRF) model was utilized to simulate wind speeds. The results indicate that the observation data are consistent with the modeled datasets regarding the relationships among the mean, standard deviation, and skewness, whereas the WRF model tends to overestimate the mean wind speeds. In addition, the predictability of annual extreme wind speeds was determined using the peak factor. Moreover, skewness has emerged as an influential parameter for predicting extreme winds. Finally, the Gram-Charlier series model was utilized to estimate probability density functions of the wind speeds, demonstrating its effectiveness in capturing positively skewed distributions. The present analyses broaden the use of both NWF outputs and statistical methods to predict extreme wind speeds in Iran.

Deep-learning-derived planetary boundary layer height from conventional meteorological measurements

Abstract. The planetary boundary layer (PBL) height (PBLH) is an important parameter for various meteorological and climate studies. This study presents a multi-structure deep neural network (DNN) model, which can estimate PBLH by integrating the morning temperature profiles and surface meteorological observations. The DNN model is developed by leveraging a rich dataset of PBLH derived from long-standing radiosonde records augmented with high-resolution micro-pulse lidar and Doppler lidar observations. We access the performance of the DNN with an ensemble of 10 members, each featuring distinct hidden-layer structures, which collectively yield a robust 27-year PBLH dataset over the southern Great Plains from 1994 to 2020. The influence of various meteorological factors on PBLH is rigorously analyzed through the importance test. Moreover, the DNN model's accuracy is evaluated against radiosonde observations and juxtaposed with conventional remote sensing methodologies, including Doppler lidar, ceilometer, Raman lidar, and micro-pulse lidar. The DNN model exhibits reliable performance across diverse conditions and demonstrates lower biases relative to remote sensing methods. In addition, the DNN model, originally trained over a plain region, demonstrates remarkable adaptability when applied to the heterogeneous terrains and climates encountered during the GoAmazon (Green Ocean Amazon; tropical rainforest) and CACTI (Cloud, Aerosol, and Complex Terrain Interactions; middle-latitude mountain) campaigns. These findings demonstrate the effectiveness of deep learning models in estimating PBLH, enhancing our understanding of boundary layer processes with implications for improving the representation of PBL in weather forecasting and climate modeling.

Design and Development of a Quality Evaluation and Monitoring System for the Short-term Weather Forecast in Gansu Province

In order to further improve the development of the meteorological observation quality management system in Gansu Province, this article takes the organic integration of quality management and meteorological observation as the concept, and combines practical business applications to develop a scoring model to quantitatively evaluate the quality of short-term warning signals and observation data transmission in Gansu Province. At the same time, a visual model is developed to achieve a personalized monitoring and analysis system for the entire process of “monitoring warning service”. System users can monitor the actual situation of various meteorological disasters, analyze disasters, and assess risks in the system, and display and apply decision information, thereby improving the scientific and standardized level of meteorological observation business management.

Hourly land surface temperature retrieval over the Tibetan Plateau using Geo-LightGBM framework: Fusion of Himawari-8 satellite, ERA5 and site observations

The Tibetan Plateau (TP) is highly sensitive to even minor fluctuations in land surface temperature (LST), which can result in permafrost melting and degradation of alpine grasslands, leading to serious ecological consequences. Therefore, it is crucial to have high-temporal-resolution and seamless hourly estimating and monitoring of LST for a better understanding of climate change on the TP. Here, we employed Himawari-8 satellite, Digital Elevation Model (DEM), ERA5 reanalysis and meteorological station observations data to develop a new LightGBM framework (called Geo-LightGBM) for estimating LST on the TP, and then analyzed the spatiotemporal variations of those LST. Geo-LightGBM demonstrated excellent LST estimation accuracy, with an R2 (coefficient of determination) of 0.971, RMSE (root-mean-square error) of 2.479 °C, and MAE (mean absolute error) of 1.510 °C. The estimated LST values for the year 2020 agreed well with observed values, with remarkable differences in hourly LST variations. Meanwhile, the estimated LST was more accurate than that from FY-4A. Spatially, there were two high LST centers, located in the Yarlung Zangbo River Basin and the Qaidam Basin, and a low LST center located in the central TP. The SHAP (SHapley Additive exPlanations) and correlation analyses revealed DSCS (the mean ground downward shortwave radiation under clear-sky conditions) to be the most importantly input variable for estimating LST. Spatiotemporal dummy variables (e.g., longitude, latitude, DEM) were also found to be crucial for model accuracy improvement. Our findings indicate the potential for constructing a high-precision and seamless 24-h LST real-time retrieval and monitoring platform for the TP by combining satellite and China's independently developed CLDAS (China Land Data Assimilation System) data in future.

Improving atmospheric pressure vertical correction model using Gaussian function

The Zenith Hydrostatic Delay (ZHD) is essential for high-precision Global Navigation Satellite System (GNSS) and Very Long Baseline Interferometry (VLBI) data processing. Accurate estimation of ZHD relies on in situ atmospheric pressure, which is primarily variable in the vertical direction. Current atmospheric pressure is either site-specific or has limited spatial coverage, necessitating vertical corrections for broader applicability. This study introduces a model that uses a Gaussian function for the vertical correction of atmospheric pressure when in situ meteorological observations are unavailable. Validation with the fifth-generation European Centre for Medium-Range Weather Forecasts reanalysis (ERA5) reveals an average Bias and RMS for the new model of 0.31 hPa and 2.96 hPa, respectively. This corresponds to improvements of 37.5% and 80.3% in terms of RMS compared to two commonly used models (T0 and Tv models) that require in situ meteorological observations, respectively. Additional validation with radiosonde data shows an average Bias and RMS of 1.85 hPa and 4.87 hPa, corresponding to the improvement of 42.8% and 71.1% in RMS compared with T0 and Tv models, respectively. These accuracies are sufficient for calculating ZHD to an accuracy of 1 mm by performing atmospheric pressure vertical correction. The new model can correct atmospheric pressure from meteorological stations or numerical weather forecasts to different heights of the troposphere.

Assessing climate change impacts on water availability in southern Portugal using a daily sequential soil-water budget model

ABSTRACT Climate change poses a significant threat to water resources in southern Portugal. To understand the potential impacts on surface and groundwater resources, this study employed a daily sequential soil-water budget model (BALSEQ) under two representative concentration pathway (RCP) scenarios, RCP 4.5 and 8.5, spanning the periods 2020–2060 and 2061–2100. An analysis of meteorological observations and EURO-CORDEX climate projections showed a decline in annual rainfall, particularly under RCP 8.5. Monthly rainfall patterns exhibited significant decreases occurring from March to June, while in December and January, rainfall is likely to increase in all RCP scenarios and respective time-windows. Potential evapotranspiration (PET) demonstrated an increase across all scenarios. BALSEQ model simulations showed a likely decrease in deep infiltration (Ip) and direct runoff (Ed) under both RCP scenarios. The river basins with the highest reductions in Ed and Ip are Mira, Barlavento, southern Guadiana, and Sotavento. For instance, Ed is projected to decrease by up to 87% in Guadiana (RCP 8.5 [2020–2060]). Similarly, Ip is projected to decrease by up to 60% in Sotavento (RCP 8.5 [2061–2100]). These findings highlight the need for adaptation and mitigation measures to preserve the region's water resources.

Distribution Characteristics of the Short-term Heavy Rainfall of an MCC in Shandong Province

This study employs a comprehensive approach, utilizing conventional meteorological observation data, Fengyun satellite cloud imagery, and ERA5 reanalysis data to examine a short-term heavy rainfall event and its underlying causes in Shandong Province from August 14 to 16, 2020. The findings reveal that: (1) This event represents a typical Mesoscale Convective Complex (MCC)-induced rainfall process, with the primary precipitation zone in central and southern Shandong. The most intense hourly rainfall is observed along the side of the MCC with the steepest Tropical Brightness Temperature Blackbody (TBB) gradient. The heaviest hourly rainfall happens right around the time of the lowest TBB interval. (2) The rainfall event is marked by a vertical atmospheric structure with a high-altitude westerly jet and a low-level southwest jet, which makes it easy for water vapour to move and shear lines to form. Favourable conditions for water vapour convergence and high specific humidity are noted. During episodes of heavy precipitation, there is a marked upward motion in the lower troposphere, aligning with the vertical pattern of low-level convergence and high-level divergence.

Spatiotemporal dependence of compound drought–heatwave and fire activity in China

Climate strongly influences fire activity, and the alignment of multiple weather and climate extremes, such as co-occurrence of hot and dry, leads to more severe fires. Recognizing the knowledge gap on the combination of compound drought–heatwave events and fire activities, we applied an event coincidence analysis to investigate their spatiotemporal dependence in China during 2003–2020 based on remote sensing active fire data and 2 206-site meteorological observations. It is found that the spatial extent of fire activities and their simultaneous occurrence with compound drought–heatwaves both expanded. The geographical hotspots of frequent fire activities following compound drought–heatwave events were predominantly situated in southwestern and southern China. There was a significant upward trend of fire activities following compound drought–heatwave events, with the ratio of compound drought–heatwave events being followed by fire activities reached 0.70 ± 0.06 in 2013–2020, indicating a growth of 19% compared to the period of 2003–2012. Our findings contribute to a better understanding of the spatiotemporal dependence of compound drought–heatwave and fire activity and provide insights into their mitigation strategies.

Intercomparison of flux-, gradient-, and variance-based optical turbulence (C n2) parameterizations

For free-space optical communication or ground-based optical astronomy, ample data of optical turbulence strength (C n 2) are imperative but typically scarce. Turbulence conditions are strongly site dependent, so their accurate quantification requires in situ measurements or numerical weather simulations. If C n 2 is not measured directly (e.g., with a scintillometer), C n 2 parameterizations must be utilized to estimate it from meteorological observations or model output. Even though various parameterizations exist in the literature, their relative performance is unknown. We fill this knowledge gap by performing a systematic three-way comparison of a flux-, gradient-, and variance-based parameterization. Each parameterization is applied to both observed and simulated meteorological variables, and the resulting C n 2 estimates are compared against observed C n 2 from two scintillometers. The variance-based parameterization yields the overall best performance, and unlike other approaches, its application is not limited to the lowest part of the atmospheric boundary layer (i.e. the surface layer). We also show that C n 2 estimated from the output of the Weather Research and Forecasting model aligns well with observations, highlighting the value of mesoscale models for optical turbulence modeling.

Physiological Characterization of Cold Tolerant and Susceptible Pigeon Pea [Cajanus cajan (L.) Millsp.] Genotypes

Background: The cold stress adversely affects the growth, survival and reproductive capacity of plants if the minimum temperature falls below 5°C. Information’s regarding cold stress and its impact on morphological characters, physio-biochemical constituents and yield attributes in cold tolerant and susceptible pigeon pea genotypes are very limited. Keeping the above facts in to consideration, the experiments were conducted. Methods: Field experiments were conducted during 2018-19 and 2019-20 with 34 genotypes of pigeon pea including checks (NDA-1, Bahar, IPA203, NDA-2, MAL-13, ICP2275, IPACT-2, MA-6, IPACT-3, IPACT-24, Rajendra Arhar, IPA15F, Dholi-D, JBT46/27, IPACT-6, IPACT-14, IPAD1-17, IPAC 68, IPACT 22, ICPL 7035 IPA19-101, IPA19-102, IPA19-103, IPA19-104, IPA19-105, IPA19-106, IPA19-107, IPA19-108, IPA19-109, IPA19-110, IPA19-111, IPA19-112, IPA19-113, IPA19-114) in RBD at ICAR-IIPR, Kanpur. Morphological and yield Characters including plant height, scoring of cold injury in exposed plants and Number of flower drop and pod set during the cold period in tagged plants were recorded. In biochemical/ crop efficiency parameters viz: Nitrogen balance index, chlorophyll, flavonols, anthocyanin’s and NDVI were measured. The photosynthetic rate, stomatal conductance, and transpiration rate were measured at the pod formation stage. Anti-oxidant enzymes activity was assayed as per standard methods. Meteorological observations during Cold stress were recorded. Result: Based on flower/pod drop and retention the genotypes like IPA 15F, Dholi dwarf, JBT46/27, IPACT-6, IPACT-14, IPAC-1-17, IPACT-68, IPACT-22, IPA 19-101, IPA 19-102, IPA 19-103, IPA 19-104, IPA 19-105, IPA 19-107, IPA 19-108, IPA 19-109, IPA 19-110, IPA 19-111 and IPA 19-113 were found susceptible to cold stress and retaining an average 51.67 % flowers/pods. Genotypes like NDA-2, MAL-13, ICP2275, IPACT-2, MA-6, IPACT-3, IPACT-24, Rajendra arhar, IPA19-106, IPA19-112 and IPA19-114 have retained more than an average of 67.48% flowers/pods are considered tolerant to cold stress. Amongst the tolerant group NDA-2, MAL-13, ICP2275, IPACT-2, IPA19-106, IPA19-112 retained the flower/pod more than the average of check genotypes (67.57%). Biochemical/plant efficiency parameters were superior in tolerant group over susceptible group.