Remote sensing has become an important tool for modern weather forecasting since it lets us see the Earth's atmosphere and surface all the time and on a huge scale. Remote sensing uses satellites and sensors to collect data in real time on important weather variables like temperature, humidity, cloud cover, precipitation patterns, and atmospheric motion. These numbers are used in numerical weather prediction models to make forecasts more accurate and timely. Advanced remote sensing technology, such as multispectral and microwave sensors, make it easier to find extreme weather phenomena including cyclones, thunderstorms, and droughts. Also, combining with machine learning methods has greatly improved the ability to make predictions and lowered the level of uncertainty in weather forecasts. In addition, study looks at how remote sensing helps with climate monitoring, agricultural planning, safety in aviation, and catastrophe risk reduction—all of which depend on reliable meteorological data for public safety and decision-making. This paper talks about the basic ideas behind remote sensing, how it is used in meteorology, and how it might help make short- and long-term weather forecasts better. It also talks about problems that need to be solved right now, like data resolution, processing limits, and the need for better global coverage. In general, remote sensing is still very important for improving weather prediction systems, helping with disaster management, and studying climate.

Keywords - Remote Sensing, Predicting the Weather, Data from satellites, Weather Forecasting with Numbers, Monitoring the atmosphere, Climate Analysis

[1] Lagasio M, Pulvirenti L, Parodi A, Boni G, Pierdicca N, Venuti G, Realini E, Tagliaferro G, Barindelli S, Rommen B. Effect of the ingestion in the WRF model of different Sentinel- derived and GNSS-derived products: analysis of the forecasts of a high impact weather event. European Journal of Remote Sensing. 2019 Dec 18;52(sup4):16-33. [2] Zébiri A, Béréziat D, Huot E, Herlin I. Rain nowcasting from multiscale radar images. InVISAPP 2019-14th International Conference on Computer Vision Theory and Applications 2019 Feb 25 (pp. 1-9). [3] Tucker CJ. FOREWORD: Satellite Remote Sensing Beyond 2015. 2017 Jan 1. [4] Langen J, Künzi K. Millimeter wave limb sounding instruments for middle atmosphere research. InIGARSS 1991; Proceedings of the 11th Annual International Geoscience and Remote Sensing Symposium 1991 (Vol. 2, pp. 525-529). [5] Chodorek A, Chodorek RR, Yastrebov A. Weather sensing in an urban environment with the use of a uav and webrtc-based platform: A pilot study. Sensors. 2021 Oct 26;21(21):7113. [6] Balsamo G, Agusti-Parareda A, Albergel C, Arduini G, Beljaars A, Bidlot J, Blyth E, Bousserez N, Boussetta S, Brown A, Buizza R. Satellite and in situ observations for advancing global Earth surface modelling: A review. Remote Sensing. 2018 Dec 14;10(12):2038. [7] Bugliaro L, Zinner T, Keil C, Mayer B, Hollmann R, Reuter M, Thomas W. Validation of cloud property retrievals with simulated satellite radiances: a case study for SEVIRI. Atmospheric Chemistry and Physics. 2011 Jun 17;11(12):5603-24. [8] Wang W, Bieker J, Arcucci R, Quilodrán-Casas C. Data Assimilation using ERA5, ASOS, and the U-STN model for Weather Forecasting over the UK. arXiv preprint arXiv:2401.07604. 2024 Jan 15. [9] Fletcher SJ, Liston GE, Hiemstra CA, Miller SD. Assimilating MODIS and AMSR-E snow observations in a snow evolution model. Journal of Hydrometeorology. 2012 Oct;13(5):1475-92. [10] Marter RE, Rapp AD. Passive microwave precipitation detection biases: Relationship to cloud morphology. InAGU Fall Meeting Abstracts 2015 Dec (Vol. 2015, pp. H13H-1662). [11] Tragesser SG, Tuncay A. Orbital design of earth-oriented tethered satellite formations. The Journal of the Astronautical Sciences. 2005 Mar;53(1):51-64. [12] Hoffman RN, Henderson JM, Nehrkorn T. Potential of 4d-VAR for exigent forecasting of severe weather. arXiv preprint arXiv:1102.2846. 2011 Feb 14. [13] Ng NY, Gopalan H, Raghavan VS, Ooi CC. Model-Agnostic Hybrid Numerical Weather Prediction and Machine Learning Paradigm for Solar Forecasting in the Tropics. arXiv preprint arXiv:2112.04963. 2021 Dec 9. [14] Roy A, Thakur PK, Pokhriyal N, Aggarwal SP, Nikam BR, Garg V, Dhote PR, Choksey A. Intercomparison of different rainfall products and validation of wrf modelled rainfall estimation in nw Himalaya during monsoon period. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2018 Nov 15;4:351-8. [15] Loew A, Bell W, Brocca L, Bulgin CE, Burdanowitz J, Calbet X, Donner RV, Ghent D, Gruber A, Kaminski T, Kinzel J. Validation practices for satellite‐based Earth observation data across communities. Reviews of Geophysics. 2017 Sep;55(3):779-817. [16] Carminati F, Migliorini S, Ingleby B, Bell W, Lawrence H, Newman S, Hocking J, Smith A. Using reference radiosondes to characterise NWP model uncertainty for improved satellite calibration and validation. Atmospheric Measurement Techniques. 2019 Jan 7;12(1):83-106. [17] Mittaz J, Merchant CJ, Woolliams ER. Applying principles of metrology to historical Earth observations from satellites. Metrologia. 2019 Jun 1;56(3):032002. [18] Mitra AK. Use of remote sensing in weather and climate forecasts. InSocial and Economic Impact of Earth Sciences 2022 Dec 17 (pp. 77-96). Singapore: Springer Nature Singapore. [19] AghaKouchak A, Farahmand A, Melton FS, Teixeira J, Anderson MC, Wardlow BD, Hain Remote sensing of drought: Progress, challenges and opportunities. Reviews of Geophysics. 2015 Jun;53(2):452-80. [20] Gerber F, de Jong R, Schaepman ME, Schaepman-Strub G, Furrer R. Predicting missing values in spatio-temporal remote sensing data. IEEE Transactions on Geoscience and Remote Sensing. 2018 Jan 31;56(5):2841-53. [21] Chander G, Hewison TJ, Fox N, Wu X, Xiong X, Blackwell WJ. Overview of intercalibration of satellite instruments. IEEE Transactions on Geoscience and Remote Sensing. 2013 Jan 11;51(3):1056-80. [22] Opazo T, Langelaan JW. Longitudinal control of transition to powered flight for a parachute-dropped multirotor. InAIAA Scitech 2020 Forum 2020 (p. 2072).