scholarly journals Koreksi Bias Data Hujan Luaran GCM ECHAM5 Untuk Prediksi Curah Hujan Bulanan dan Musiman Pulau Lombok

2021 ◽  
Vol 7 (2) ◽  
pp. 209-219
Author(s):  
Humairo Saidah ◽  
Agustono Setiawan ◽  
Lilik Hanifah ◽  
Eko Pradjoko ◽  
Agus Suroso

This study aims to evaluate the ability of the ECHAM5 GCM model output data in estimating monthly rainfall on the island of Lombok. The data used in this study are ECHAM5 monthly rainfall data and automatic rainfall recorder (ARR) measurement rain data for 2000-2018 obtained from ARR Gunung Sari. Correction of bias is conducted by using the mean ratio method and the regression method. The method that produces the best approach is then used to obtain rain data projections and a simple regression method. Evaluation and validation used the Pearson correlation coefficient (r), Root Mean Square Error (RMSE) and Nash-Sutcliffe Efficiency (NSE) values. The results obtained are that the daily and monthly rainfall data from the ECHAM5 model cannot be directly used to replace the rain measurement data because of its very low accuracy. The downscaling technique performed on daily and monthly rainfall data using the average ratio method does not show satisfactory performance where the efficiency figures produced are still low even gave a slight increasing number. However, the ECHAM5 model data can be used to obtain rainfall projections on a monthly and seasonal scale with a good and satisfactory correlation.  Key words: mean ratio method; global climate model; ECHAM5; monthly rainfall.

2019 ◽  
Vol 11 (13) ◽  
pp. 1625 ◽  
Author(s):  
Giulio Nils Caroletti ◽  
Roberto Coscarelli ◽  
Tommaso Caloiero

Skills in reproducing monthly rainfall over Calabria (southern Italy) have been validated for the Climate Hazards group InfraRed Precipitation with Station data (CHIRPS) satellite data, the E-OBS dataset and 13 Global Climate Model-Regional Climate Model (GCM-RCM) combinations, belonging to the ENSEMBLES project output set. To this aim, 73 rainfall series for the period 1951–1980 and 79 series for the period 1981–2010 have been selected from the database managed by Multi-Risk Functional Centre of the Regional Agency for Environmental Protection (Regione Calabria). The relative mean and standard deviation errors, and the Pearson correlation coefficient have been used as validation metrics. Results showed that CHIRPS satellite data (available only for the 1981–2010 validation period) and RCMs based on the ECHAM5 Global Climate performed better both in mean error and standard deviation error compared to other datasets. Moreover, a slight appreciable improvement in performance for all ECHAM5-based models and for the E-OBS dataset has been observed in the 1981–2010 time-period. The whole validation-and-assessment procedure applied in this work is general and easily applicable where ground data and gridded data are available. This procedure might help scientists and policy makers to select among available datasets those best suited for further applications, even in regions with complex orography and an inadequate amount of representative stations.


1996 ◽  
Author(s):  
Larry Bergman ◽  
J. Gary ◽  
Burt Edelson ◽  
Neil Helm ◽  
Judith Cohen ◽  
...  

2010 ◽  
Vol 10 (14) ◽  
pp. 6527-6536 ◽  
Author(s):  
M. A. Brunke ◽  
S. P. de Szoeke ◽  
P. Zuidema ◽  
X. Zeng

Abstract. Here, liquid water path (LWP), cloud fraction, cloud top height, and cloud base height retrieved by a suite of A-train satellite instruments (the CPR aboard CloudSat, CALIOP aboard CALIPSO, and MODIS aboard Aqua) are compared to ship observations from research cruises made in 2001 and 2003–2007 into the stratus/stratocumulus deck over the southeast Pacific Ocean. It is found that CloudSat radar-only LWP is generally too high over this region and the CloudSat/CALIPSO cloud bases are too low. This results in a relationship (LWP~h9) between CloudSat LWP and CALIPSO cloud thickness (h) that is very different from the adiabatic relationship (LWP~h2) from in situ observations. Such biases can be reduced if LWPs suspected to be contaminated by precipitation are eliminated, as determined by the maximum radar reflectivity Zmax>−15 dBZ in the apparent lower half of the cloud, and if cloud bases are determined based upon the adiabatically-determined cloud thickness (h~LWP1/2). Furthermore, comparing results from a global model (CAM3.1) to ship observations reveals that, while the simulated LWP is quite reasonable, the model cloud is too thick and too low, allowing the model to have LWPs that are almost independent of h. This model can also obtain a reasonable diurnal cycle in LWP and cloud fraction at a location roughly in the centre of this region (20° S, 85° W) but has an opposite diurnal cycle to those observed aboard ship at a location closer to the coast (20° S, 75° W). The diurnal cycle at the latter location is slightly improved in the newest version of the model (CAM4). However, the simulated clouds remain too thick and too low, as cloud bases are usually at or near the surface.


2009 ◽  
Vol 29 (1) ◽  
pp. 94-101 ◽  
Author(s):  
Heiko Goelzer ◽  
Anders Levermann ◽  
Stefan Rahmstorf

2012 ◽  
Vol 43 (3) ◽  
pp. 215-230 ◽  
Author(s):  
Manish Kumar Goyal ◽  
C. S. P. Ojha

We investigate the performance of existing state-of-the-art rule induction and tree algorithms, namely Single Conjunctive Rule Learner, Decision Table, M5 Model Tree, Decision Stump and REPTree. Downscaling models are developed using these algorithms to obtain projections of mean monthly precipitation to lake-basin scale in an arid region in India. The effectiveness of these algorithms is evaluated through application to downscale the predictand for the Lake Pichola region in Rajasthan state in India, which is considered to be a climatically sensitive region. The predictor variables are extracted from (1) the National Centre for Environmental Prediction (NCEP) reanalysis dataset for the period 1948–2000 and (2) the simulations from the third-generation Canadian Coupled Global Climate Model (CGCM3) for emission scenarios A1B, A2, B1 and COMMIT for the period 2001–2100. M5 Model Tree algorithm was found to yield better performance among all other learning techniques explored in the present study. The precipitation is projected to increase in future for A2 and A1B scenarios, whereas it is least for B1 and COMMIT scenarios using predictors.


2015 ◽  
Vol 28 (20) ◽  
pp. 8093-8108 ◽  
Author(s):  
Cathryn E. Birch ◽  
Malcolm J. Roberts ◽  
Luis Garcia-Carreras ◽  
Duncan Ackerley ◽  
Michael J. Reeder ◽  
...  

Abstract There are some long-established biases in atmospheric models that originate from the representation of tropical convection. Previously, it has been difficult to separate cause and effect because errors are often the result of a number of interacting biases. Recently, researchers have gained the ability to run multiyear global climate model simulations with grid spacings small enough to switch the convective parameterization off, which permits the convection to develop explicitly. There are clear improvements to the initiation of convective storms and the diurnal cycle of rainfall in the convection-permitting simulations, which enables a new process-study approach to model bias identification. In this study, multiyear global atmosphere-only climate simulations with and without convective parameterization are undertaken with the Met Office Unified Model and are analyzed over the Maritime Continent region, where convergence from sea-breeze circulations is key for convection initiation. The analysis shows that, although the simulation with parameterized convection is able to reproduce the key rain-forming sea-breeze circulation, the parameterization is not able to respond realistically to the circulation. A feedback of errors also occurs: the convective parameterization causes rain to fall in the early morning, which cools and wets the boundary layer, reducing the land–sea temperature contrast and weakening the sea breeze. This is, however, an effect of the convective bias, rather than a cause of it. Improvements to how and when convection schemes trigger convection will improve both the timing and location of tropical rainfall and representation of sea-breeze circulations.


2010 ◽  
Vol 10 (12) ◽  
pp. 5449-5474 ◽  
Author(s):  
M. Wang ◽  
J. E. Penner

Abstract. A statistical cirrus cloud scheme that accounts for mesoscale temperature perturbations is implemented in a coupled aerosol and atmospheric circulation model to better represent both subgrid-scale supersaturation and cloud formation. This new scheme treats the effects of aerosol on cloud formation and ice freezing in an improved manner, and both homogeneous freezing and heterogeneous freezing are included. The scheme is able to better simulate the observed probability distribution of relative humidity compared to the scheme that was implemented in an older version of the model. Heterogeneous ice nuclei (IN) are shown to decrease the frequency of occurrence of supersaturation, and improve the comparison with observations at 192 hPa. Homogeneous freezing alone can not reproduce observed ice crystal number concentrations at low temperatures (<205 K), but the addition of heterogeneous IN improves the comparison somewhat. Increases in heterogeneous IN affect both high level cirrus clouds and low level liquid clouds. Increases in cirrus clouds lead to a more cloudy and moist lower troposphere with less precipitation, effects which we associate with the decreased convective activity. The change in the net cloud forcing is not very sensitive to the change in ice crystal concentrations, but the change in the net radiative flux at the top of the atmosphere is still large because of changes in water vapor. Changes in the magnitude of the assumed mesoscale temperature perturbations by 25% alter the ice crystal number concentrations and the net radiative fluxes by an amount that is comparable to that from a factor of 10 change in the heterogeneous IN number concentrations. Further improvements on the representation of mesoscale temperature perturbations, heterogeneous IN and the competition between homogeneous freezing and heterogeneous freezing are needed.


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