bias adjustment
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Climate ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 181
Author(s):  
Alice Crespi ◽  
Marcello Petitta ◽  
Paola Marson ◽  
Christian Viel ◽  
Lucas Grigis

This work discusses the ability of a bias-adjustment method using empirical quantile mapping to improve the skills of seasonal forecasts over Europe for three key climate variables, i.e., temperature, precipitation and wind speed. In particular, the suitability of the approach to be integrated in climate services and to provide tailored predictions for local applications was evaluated. The workflow was defined in order to allow a flexible implementation and applicability while providing accurate results. The scheme adjusted monthly quantities from the seasonal forecasting system SEAS5 of the European Centre for Medium-Range Forecasts (ECMWF) by using ERA5 reanalysis as reference. Raw and adjusted forecasts were verified through several metrics analyzing different aspects of forecast skills. The applied method reduced model biases for all variables and seasons even though more limited improvements were obtained for precipitation. In order to further assess the benefits and limitations of the procedure, the results were compared with those obtained by the ADAMONT method, which calibrates daily quantities by empirical quantile mapping conditioned by weather regimes. The comparable performances demonstrated the overall suitability of the proposed method to provide end users with calibrated predictions of monthly and seasonal quantities.


2021 ◽  
Author(s):  
Mégane Alavoine ◽  
Patrick Grenier

Bias adjustment of numerical climate model simulations involves several technical and epistemological arguments wherein the notion of physical inconsistency is often referred to, either for rejecting the legitimacy of bias adjustment in general or for justifying the necessity of sophisticated multivariate techniques. However, this notion is often mishandled, in part because the literature generally proceeds without defining it. In this context, the central objective of this study is to clarify and illustrate the distinction between physical inconsistency and multivariate bias, by investigating the effect of bias adjustment on two different kinds of inter-variable relationships, namely a physical constraint expected to hold at every step of a time series and statistical properties that emerge with potential bias over a climatic time scale. The study involves the application of 18 alternative bias adjustment techniques on 10 climate simulations and over 12 sites across North America. Adjusted variables are temperature, pressure, relative humidity and specific humidity, linked by a thermodynamic constraint. The analysis suggests on the one hand that a clear instance of potential physical inconsistency can be avoided with either a univariate or a multivariate technique, if and only if the bias adjustment strategy explicitly considers the physical constraint to be preserved. On the other hand, it also suggests that sophisticated multivariate techniques alone aren’t complete adjustment strategies in presence of a physical constraint, as they cannot replace its explicit consideration. As a supplementary objective, this study relates common optional adjustment procedures with likely effects on diverse basic statistical properties, as an effort to guide climate information users in the determination of adequate bias adjustment strategies for their research purposes.


2021 ◽  
Author(s):  
Fabian Lehner ◽  
Imran Nadeem ◽  
Herbert Formayer

Abstract. Daily meteorological data such as temperature or precipitation from climate models is needed for many climate impact studies, e.g. in hydrology or agriculture but direct model output can contain large systematic errors. Thus, statistical bias adjustment is applied to correct climate model outputs. Here we review existing statistical bias adjustment methods and their shortcomings, and present a method which we call EQA (Empirical Quantile Adjustment), a development of the methods EDCDFm and PresRAT. We then test it in comparison to two existing methods using real and artificially created daily temperature and precipitation data for Austria. We compare the performance of the three methods in terms of the following demands: (1): The model data should match the climatological means of the observational data in the historical period. (2): The long-term climatological trends of means (climate change signal), either defined as difference or as ratio, should not be altered during bias adjustment, and (3): Even models with too few wet days (precipitation above 0.1 mm) should be corrected accurately, so that the wet day frequency is conserved. EQA fulfills (1) almost exactly and (2) at least for temperature. For precipitation, an additional correction included in EQA assures that the climate change signal is conserved, and for (3), we apply another additional algorithm to add precipitation days.


2021 ◽  
Author(s):  
Maialen Iturbide ◽  
Ana Casanueva ◽  
Joaquín Bedia ◽  
Sixto Herrera ◽  
Josipa Milovac ◽  
...  

2021 ◽  
pp. 1-59
Author(s):  
Abhishek Savita ◽  
Catia M. Domingues ◽  
Tim Boyer ◽  
Viktor Gouretski ◽  
Masayoshi Ishii ◽  
...  

AbstractThe Earth system is accumulating energy due to human-induced activities. More than 90% of this energy has been stored in the ocean as heat since 1970, with ~64% of that in the upper 700 m. Differences in upper ocean heat content anomaly (OHCA) estimates, however, exist. Here, we use a dataset protocol for 1970–2008 – with six instrumental bias adjustments applied to expendable bathythermograph (XBT) data, and mapped by six research groups – to evaluate the spatio-temporal spread in upper OHCA estimates arising from two choices: firstly, those arising from instrumental bias adjustments; and secondly those arising from mathematical (i.e. mapping) techniques to interpolate and extrapolate data in space and time. We also examined the effect of a common ocean mask, which reveals that exclusion of shallow seas can reduce global OHCA estimates up to 13%. Spread due to mapping method is largest in the Indian Ocean and in the eddy-rich and frontal regions of all basins. Spread due to XBT bias adjustment is largest in the Pacific Ocean within 30°N–30°S. In both mapping and XBT cases, spread is higher for 1990–2004. Statistically different trends among mapping methods are not only found in the poorly-observed Southern Ocean but also on the well-observed Northwest Atlantic. Our results cannot determine the best mapping or bias adjustment schemes but they identify where important sensitivities exist, and thus where further understanding will help to refine OHCA estimates. These results highlight the need for further coordinated OHCA studies to evaluate the performance of existing mapping methods along with comprehensive assessment of uncertainty estimates.


2021 ◽  
Author(s):  
Chris J. Kennedy ◽  
Jayson S. Marwaha ◽  
P. Nina Scalise ◽  
Kortney A. Robinson ◽  
Brandon Booth ◽  
...  

Background: Post-discharge opioid consumption is an important source of data in guiding appropriate opioid prescribing guidelines, but its collection is tedious and requires significant resources. Furthermore, the reliability of post-discharge opioid consumption surveys is unclear. Our group developed an automated short messaging service (SMS)-to-web survey for collecting this data from patients. In this study, we assessed its effectiveness in estimating opioid consumption by performing causal adjustment and comparison to a phone-based survey as reference. Methods: Patients who underwent surgical procedures at our institution from 2019-2020 were sent an SMS message with a link to a secure web survey to quantify opioids consumed after discharge. Several patient factors extracted from the EHR were tested for association with survey response. Following targeted learning (TL) nonresponse adjustment using these EHR-based factors, opioid consumption survey results were compared to a prior telephone-based survey at our institution as a reference. Results: 6,553 patients were included. Opioid consumption was measured in 2,883 (44%), including 1,342 (20.5%) through survey response. Characteristics associated with inability to measure opioid consumption included age, length of stay, race, tobacco use, and missing preoperative assessment. Among the top 10 procedures by volume, EHR-based TL nonresponse bias adjustment corrected the median opioid consumption reported by an average of 57%, and corrected the 75th percentile of reported consumption by an average of 11%. This brought median estimates for 6/10 procedures closer to telephone survey-based consumption estimates, and 75th percentile estimates for 3/10 procedures closer to telephone survey-based consumption estimates. Conclusion: We found that applying electronic health record (EHR)-based machine learning nonresponse bias adjustment is essential for debiased opioid consumption estimates from patient surveys. After adjustment, post-discharge surveys can generate reliable opioid consumption estimates. Clinical factors from the EHR combined with TL adjustment appropriately capture differences between responders and nonresponders and should be used prior to generalizing or applying opioid consumption estimates to patient care.


2021 ◽  
Author(s):  
Mika Rantanen ◽  
Matti Kämäräinen ◽  
Otto Hyvärinen ◽  
Andrea Vajda

<p>Sub-seasonal to seasonal scale forecasts provide useful information for city authorities for operational planning, preparedness and maintenance costs optimization. In the EU H2020 E-SHAPE project the Finnish Meteorological Institute aims at developing an operational service providing user-oriented sub-seasonal and seasonal forecast products for the City of Helsinki tailored for winter maintenance activities. To be able to provide skilful sub-seasonal to seasonal forecasts products, bias adjustment and evaluation of the used weather parameters, i.e. temperature and snow is crucial. </p><p>In this study, we focus on the skill assessment of sub-seasonal temperature forecasts in Helsinki, Finland, experimenting with various methods to adjust the bias from the raw temperature forecasts. Due to its coastal location, skilful forecasting of temperatures for Helsinki is challenging. The temperature gradient on the coastline is especially strong during spring when inland areas warm considerably faster than the coastline. Therefore, raw point forecasts for Helsinki suffer from cold bias during the March-July period.</p><p>We use the 2 m temperature extended-range reforecasts obtained from the ECMWF S2S database and apply two bias adjustment techniques: removing the mean bias and the quantile mapping method. Reforecasts for a 20-years period, 2000-2019 with 10 ensemble members, run twice a week for 46 days ahead were calibrated and evaluated. Two datasets are used as reference, observations from Helsinki Kaisaniemi weather station and gridded ERA5 reanalysis data. Thus, these combinations yield in total five sets of forecasts which are evaluated against the observations.</p><p>The results of the experiments and the potential added value of bias correction will be presented for discussion. Based on the preliminary results, especially the cold bias in spring and early summer can be improved with the bias-correction methods. The bias-adjusted extended-range temperature forecasts are used in the development of sub-seasonal winter forecast products tailored for the needs of city maintenance.</p>


2021 ◽  
Author(s):  
Anita Verpe Dyrrdal ◽  
Hans Olav Hygen ◽  
Irene Brox Nilsen ◽  
Stephanie Mayer

<p>In the wake of the 6th assessment report from IPCC due this year, the Norwegian Centre for Climate Services (NCCS) has started a project to update their national climate assessment report Climate in Norway 2100. A major part of this update revolves around the selection of a representative model ensemble for a low, medium and high emission scenario, plus bias adjustment of EURO-CORDEX output and statistical downscaling directly from CMIP6 to the national, and subnational, level. The results will form the natural scientific basis for local climate adaptation in Norway, through the computation of expected changes in selected climate indices on a 1 x 1 km grid covering the Norwegian mainland. </p><p>The new knowledge will also serve to update the much used climate fact sheets (presented at EMS 2016) for Norwegian counties. We aim to develop a map based webtool for the climate fact sheets, consisting of map layers of several climate indices. The user will be able to get tailored fact sheets for a given point or region, generated from a template that merges information from map layers and predefined texts.</p><p>The project is divided into five working groups: 1. Historical climate, 2. Modeling, 2. Future climate, 4. Infrastructure, 5. Outreach. In this presentation we will present the organization and plans for the project, as well as details on the model ensemble selection from EURO-CORDEX, based on both CMIP5 and CMIP6, and the methods for downscaling a bias-adjustment to the national level. The updated report is planned to be issued in 2024.</p>


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