Multi-model ensemble predictions of precipitation and temperature using machine learning algorithms

Ahmed, Kamal; Sachindra, D. A.; Shahid, Shamsuddin; Iqbal, Zafar; Nawaz, Nadeem; Khan, Najeebullah

doi:10.1016/j.atmosres.2019.104806

Cited by 143 publications

(80 citation statements)

References 64 publications

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“…Besides, there are no well-established guidelines for the selection of appropriate GCMs. However, it is expected that the selected GCM will be able to replicate the mean, spatial variability, and distribution of historical climate (Ahmed et al 2020). It is also suggested that the selection of GCMs based on their performance in simulating both rainfall and temperature as both are equally required for most of the climate change studies (Ahmed et al 2019a;Nashwan and Shahid 2020;Shiru et al 2020) GCM simulations disseminated through different phases of coupled model intercomparison project (CMIP) are vital sources for quantitative climate projection over the twenty-first century (Baker and Huang 2014;Eyring et al 2016).…”

Section: Introductionmentioning

confidence: 99%

Comparison of CMIP6 and CMIP5 model performance in simulating historical precipitation and temperature in Bangladesh: a preliminary study

Kamruzzaman

Shahid

Islam

et al. 2021

Theor Appl Climatol

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The relative performance of global climate models (GCMs) of phases 5 and 6 of the Coupled Model Intercomparison Project (CMIP5 and CMIP6, respectively) was assessed in this study based on their ability to simulate annual and seasonal mean rainfall and temperature over Bangladesh for the period 1977-2005. The multiple statistical metrics were used to measure the performance of the GCMs at 30 meteorological observation stations. Two robust multicriteria decision analysis methods were used to integrate the results obtained using different metrics for an unbiased ranking of the GCMs. The results revealed MIROC5 as the most skilful among CMIP5 GCMs and ACCESS-CM2 among CMIP6 GCMs. Overall, a significant improvement in CMIP6 MME compared to CMIP5 MME was noticed in simulating rainfall over Bangladesh at annual and seasonal scales. CMIP6 MME also showed significant reduction in maximum and minimum temperature biases over Bangladesh. However, systematic wet and cold biases still exist in CMIP6 models for Bangladesh. CMIP6 GCMs showed higher spatial correlation with observed data compared to CMIP5 GCMs, but higher difference in terms of standard deviations and centered root mean square errors, indicating better performance in simulating geographical distribution but lower performance in simulating spatial variability of most of the climate variables for different timescales. In terms of Taylor skill score, the CMIP6 MME showed higher performance in simulating rainfall but lower performance in simulating temperature compared to CMIP5 MME for most of the timeframes. The findings of this study suggest that the added value of rainfall and temperature simulations in CMIP6 models is incompatible with the climate models used in this research.

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Section: Introductionmentioning

confidence: 99%

Comparison of CMIP6 and CMIP5 model performance in simulating historical precipitation and temperature in Bangladesh: a preliminary study

Kamruzzaman

Shahid

Islam

et al. 2021

Theor Appl Climatol

Self Cite

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“…The KNN is a relatively simple learning algorithm that can be used for both classification and regression problems. The KNN has been used in several remote sensing applications such as precipitation types classifications (e.g., Yang et al, 2019), precipitation estimation (Ahmed et al, 2020; Huang et al, 2017), and image classification (e.g., Li et al, 2009). The KNN assumes that features with similar properties exist in a close proximity.…”

Section: Methodsmentioning

confidence: 99%

Comparative Assessment of Snowfall Retrieval From Microwave Humidity Sounders Using Machine Learning Methods

Adhikari

Ehsani

Song

et al. 2020

Earth and Space Science

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Accurate quantification of snowfall rate from space is important, but has remained difficult. Four years (2007-2010) of NOAA-18 Microwave Humidity Sounder (MHS) data are trained and tested with snowfall estimates from coincident CloudSat Cloud Profiling Radar (CPR) observations using several machine learning methods. Among the studied methods, random forest using MHS (RF-MHS) is found the best for both detection and estimation of global snowfall. The RF-MHS estimates are tested using independent years of coincident CPR snowfall estimates and compared with snowfall rates from Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2), Atmospheric Infrared Sounder (AIRS), and MHS Goddard Profiling Algorithm (GPROF). It was found that RF-MHS algorithm can detect global snowfall with approximately 90% accuracy and a Heidke skill score of 0.48 compared to independent CloudSat samples. The surface wet bulb temperatures, brightness temperatures at 190 GHz, and 157 GHz channels are found to be the most important features to delineate snowfall areas. The RF-MHS retrieved global snowfall rates are well compared with CPR estimates and show generally better statistics than MERRA-2, AIRS, and GPROF ©2020 American Geophysical Union. All rights reserved. products. A case study over the US verifies that the RF-MHS estimated snowfall agrees well with the ground-based NCEP Stage-IV and MERRA-2 product whereas a relatively large underestimation is observed with the current GPROF product (V05). MHS snowfall estimated based on RF algorithm, however, shows some underestimation over cold and snow-covered surfaces (e.g., Greenland, Alaska, and Northern Russia), where improvements through new sensors or retrieval techniques are needed.

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“…The third group of activities evaluates or ranks different GCMs or RCMs. Ahmed et al (2020) report on downscaling monthly precipitation, maximum and minimum temperatures using ANN, K-nearest neighbour (KNN), SVM and RVM. They rank 15 GCMs and report HadGEM2-AO as the most skilled model and conclude that KNN and RVM exhibit higher performance than SVM and ANN.…”

Section: Introductionmentioning

confidence: 99%

Statistical downscaling of precipitation using inclusive multiple modelling (IMM) at two levels

Sadeghfam

Khatibi²,

Moradian

et al. 2021

Journal of Water and Climate Change

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Topical research on hydrological impacts of climate change in terms of downscaling of monthly precipitation is investigated in this paper by formulating an inclusive multiple modelling (IMM) strategy. IMM strategies manage multiple models at two levels and the paper uses statistical downscaling model, Sugeno fuzzy logic and support vector machine at Level 1 and feeds their outputs to a neuro-fuzzy model at Level 2. In the downscaling stage, large-scale NCEP (National Centres for Environmental Prediction)/NCAR (National Centre for Atmospheric Research) data for a station with local data record from 1961 to 2005 are used for training and testing Level 1 models, which are found to be ‘fit-for-purpose’, but the variations between them signify some room for improvements. The model at Level 2 combines outputs of those at Level 1 and produces Level 2 results, which are over the Level 1 models in terms of dispersion of residual errors. In this way, IMM provides a more defensible modelling strategy for application in the projection stage. The comparison between observed and projected precipitation indicates that precipitation will be likely to reduce compared with observed precipitation in cold seasons (October–February), but the projected precipitation will be likely to increase slightly in wet seasons (April and May).

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Multi-model ensemble predictions of precipitation and temperature using machine learning algorithms

Cited by 143 publications

References 64 publications

Comparison of CMIP6 and CMIP5 model performance in simulating historical precipitation and temperature in Bangladesh: a preliminary study

Comparison of CMIP6 and CMIP5 model performance in simulating historical precipitation and temperature in Bangladesh: a preliminary study

Comparative Assessment of Snowfall Retrieval From Microwave Humidity Sounders Using Machine Learning Methods

Statistical downscaling of precipitation using inclusive multiple modelling (IMM) at two levels

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