The Annals of Applied Statistics

Distributional regression forests for probabilistic precipitation forecasting in complex terrain

Lisa Schlosser, Torsten Hothorn, Reto Stauffer, and Achim Zeileis

Full-text: Access denied (no subscription detected)

We're sorry, but we are unable to provide you with the full text of this article because we are not able to identify you as a subscriber. If you have a personal subscription to this journal, then please login. If you are already logged in, then you may need to update your profile to register your subscription. Read more about accessing full-text

Abstract

To obtain a probabilistic model for a dependent variable based on some set of explanatory variables, a distributional approach is often adopted where the parameters of the distribution are linked to regressors. In many classical models this only captures the location of the distribution but over the last decade there has been increasing interest in distributional regression approaches modeling all parameters including location, scale and shape. Notably, so-called nonhomogeneous Gaussian regression (NGR) models both mean and variance of a Gaussian response and is particularly popular in weather forecasting. Moreover, generalized additive models for location, scale and shape (GAMLSS) provide a framework where each distribution parameter is modeled separately capturing smooth linear or nonlinear effects. However, when variable selection is required and/or there are nonsmooth dependencies or interactions (especially unknown or of high-order), it is challenging to establish a good GAMLSS. A natural alternative in these situations would be the application of regression trees or random forests but, so far, no general distributional framework is available for these. Therefore, a framework for distributional regression trees and forests is proposed that blends regression trees and random forests with classical distributions from the GAMLSS framework as well as their censored or truncated counterparts. To illustrate these novel approaches in practice, they are employed to obtain probabilistic precipitation forecasts at numerous sites in a mountainous region (Tyrol, Austria) based on a large number of numerical weather prediction quantities. It is shown that the novel distributional regression forests automatically select variables and interactions, performing on par or often even better than GAMLSS specified either through prior meteorological knowledge or a computationally more demanding boosting approach.

Article information

Source
Ann. Appl. Stat., Volume 13, Number 3 (2019), 1564-1589.

Dates
Received: November 2018
Revised: February 2019
First available in Project Euclid: 17 October 2019

Permanent link to this document
https://projecteuclid.org/euclid.aoas/1571277764

Digital Object Identifier
doi:10.1214/19-AOAS1247

Mathematical Reviews number (MathSciNet)
MR4019150

Zentralblatt MATH identifier
07145968

Keywords
Parametric models regression trees random forests recursive partitioning probabilistic forecasting GAMLSS

Citation

Schlosser, Lisa; Hothorn, Torsten; Stauffer, Reto; Zeileis, Achim. Distributional regression forests for probabilistic precipitation forecasting in complex terrain. Ann. Appl. Stat. 13 (2019), no. 3, 1564--1589. doi:10.1214/19-AOAS1247. https://projecteuclid.org/euclid.aoas/1571277764


Export citation

References

  • Athey, S., Tibshirani, J. and Wager, S. (2019). Generalized random forests. Ann. Statist. 47 1148–1178.
  • Baran, S. and Nemoda, D. (2016). Censored and shifted gamma distribution based EMOS model for probabilistic quantitative precipitation forecasting. Environmetrics 27 280–292.
  • Bauer, P., Thorpe, A. and Brunet, G. (2015). The quiet revolution of numerical weather prediction. Nature 525 (7567) 47–55.
  • Biau, G. and Scornet, E. (2016). A random forest guided tour. TEST 25 197–227.
  • BMLFUW (2016). Bundesministerium für Land und Forstwirtschaft, Umwelt und Wasserwirtschaft (BMLFUW), Abteilung IV/4—Wasserhaushalt. Available at http://ehyd.gv.at/. Accessed: 2016–02–29.
  • Box, G. E. P. and Cox, D. R. (1964). An analysis of transformations. (With discussion). J. Roy. Statist. Soc. Ser. B 26 211–252.
  • Breiman, L. (2001). Random forests. Mach. Learn. 45 5–32.
  • Breiman, L., Friedman, J. H., Olshen, R. A. and Stone, C. J. (1984). Classification and Regression Trees. Wadsworth Statistics/Probability Series. Wadsworth Advanced Books and Software, Belmont, CA.
  • Dunn, P. K. and Smyth, G. K. (1996). Randomized quantile residuals. J. Comput. Graph. Statist. 5 236–244.
  • Gebetsberger, M., Messner, J. W., Mayr, G. J. and Zeileis, A. (2017). Fine-tuning non-homogeneous regression for probabilistic precipitation forecasts: Unanimous predictions, heavy tails, and link functions. Mon. Weather Rev. 145 4693–4708.
  • Glahn, H. R. and Lowry, D. A. (1972). The use of model output statistics (MOS) in objective weather forecasting. J. Appl. Meteorol. 11 1203–1211.
  • Gneiting, T., Balabdaoui, F. and Raftery, A. E. (2007). Probabilistic forecasts, calibration and sharpness. J. R. Stat. Soc. Ser. B. Stat. Methodol. 69 243–268.
  • Gneiting, T. and Raftery, A. E. (2007). Strictly proper scoring rules, prediction, and estimation. J. Amer. Statist. Assoc. 102 359–378.
  • Gneiting, T., Raftery, A. E., Westveld III, A. H. and Goldman, T. (2005). Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation. Mon. Weather Rev. 133 1098–1118.
  • Goicoa, T., Adin, A., Ugarte, M. D. and Hodges, J. S. (2018). In spatio-temporal disease mapping models, identifiability constraints affect PQL and INLA results. Stoch. Environ. Res. Risk Assess. 32 749–770.
  • Hamill, T. M., Bates, G. T., Whitaker, J. S., Murray, D. R., Fiorino, M., Galarneau Jr., T. J., Zhu, Y. and Lapenta, W. (2013). NOAA’s second-generation global medium-range ensemble reforecast dataset. Bull. Am. Meteorol. Soc. 94 1553–1565.
  • Hastie, T. and Tibshirani, R. (1986). Generalized additive models. Statist. Sci. 1 297–318.
  • Hastie, T., Tibshirani, R. and Friedman, J. (2001). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer Series in Statistics. Springer, New York.
  • Hersbach, H. (2000). Decomposition of the continuous ranked probability score for ensemble prediction systems. Weather Forecast. 15 559–570.
  • Hofner, B., Mayr, A. and Schmid, M. (2016). gamboostLSS: An R package for model building and variable selection in the GAMLSS framework. J. Stat. Softw. 74 (1) 1–31.
  • Hothorn, T., Hornik, K. and Zeileis, A. (2006). Unbiased recursive partitioning: A conditional inference framework. J. Comput. Graph. Statist. 15 651–674.
  • Hothorn, T. and Zeileis, A. (2017). Transformation forests. Available at arXiv:1701.02110.
  • Hothorn, T., Lausen, B., Benner, A. and Radespiel-Tröger, M. (2004). Bagging survival trees. Stat. Med. 23 77–91.
  • Hothorn, T., Hornik, K., van de Wiel, M. A. and Zeileis, A. (2006). A Lego system for conditional inference. Amer. Statist. 60 257–263.
  • Hutchinson, M. F. (1998). Interpolation of rainfall data with thin plate smoothing splines—Part II: Analysis of topographic dependence. Journal of Geographic Information and Decision Analysis 2 152–167.
  • Klein, N., Kneib, T., Lang, S. and Sohn, A. (2015). Bayesian structured additive distributional regression with an application to regional income inequality in Germany. Ann. Appl. Stat. 9 1024–1052.
  • Lin, Y. and Jeon, Y. (2006). Random forests and adaptive nearest neighbors. J. Amer. Statist. Assoc. 101 578–590.
  • Long, J. S. (1997). Regression Models for Categorical and Limited Dependent Variables. Sage Publications, Thousand Oaks, CA.
  • Meinshausen, N. (2006). Quantile regression forests. J. Mach. Learn. Res. 7 983–999.
  • Messner, J. W., Mayr, G. J. and Zeileis, A. (2016). Heteroscedastic censored and truncated regression with crch. The R Journal 8 (1) 173–181.
  • Messner, J. W., Mayr, G. J. and Zeileis, A. (2017). Non-homogeneous boosting for predictor selection in ensemble post-processing. Mon. Weather Rev. 145 137–147.
  • Nelder, J. A. and Wedderburn, R. W. M. (1972). Generalized linear models. J. R. Stat. Soc. Ser. A 135 370–384.
  • Rasp, S. and Lerch, S. (2018). Neural networks for post-processing ensemble weather forecasts. Mon. Weather Rev. 146 3885–3900.
  • Rigby, R. A. and Stasinopoulos, D. M. (2005a). Generalized additive models for location, scale and shape. J. R. Stat. Soc. Ser. C. Appl. Stat. 54 507–554.
  • Robinson, N., Regetz, J. and Guralnick, R. P. (2014). EarthEnv-DEM90: A nearly-global, void-free, multi-scale smoothed, 90m digital elevation model from fused ASTER and SRTM data. ISPRS J. Photogramm. Remote Sens. 87 57–67.
  • Scheuerer, M. and Hamill, T. M. (2015). Statistical post-processing of ensemble precipitation forecasts by fitting censored, shifted gamma distributions. Mon. Weather Rev. 143 4578–4596.
  • Schlosser, L., Hothorn, T., Stauffer, R. and Zeileis, A. (2019a). Different response distributions. Supplement A to “Distributional regression forests for probabilistic precipitation forecasting in complex terrain.” DOI:10.1214/19-AOAS1247SUPPA.
  • Schlosser, L., Hothorn, T., Stauffer, R. and Zeileis, A. (2019b). Stationwise evaluation. Supplement B to “Distributional regression forests for probabilistic precipitation forecasting in complex terrain.” DOI:10.1214/19-AOAS1247SUPPB.
  • Stasinopoulos, D. M. and Rigby, R. A. (2007). Generalized additive models for location scale and shape (GAMLSS) in R. J. Stat. Softw. 23 (7) 1–46.
  • Stauffer, R., Umlauf, N., Messner, J. W., Mayr, G. J. and Zeileis, A. (2017a). Ensemble post-processing of daily precipitation sums over complex terrain using censored high-resolution standardized anomalies. Mon. Weather Rev. 45 955–969.
  • Stauffer, R., Mayr, G. J., Messner, J. W., Umlauf, N. and Zeileis, A. (2017b). Spatio-temporal precipitation climatology over complex terrain using a censored additive regression model. Int. J. Climatol. 37 3264–3275.
  • Stidd, C. K. (1973). Estimating the precipitation climate. Water Resour. Res. 9 1235–1241.
  • Strasser, H. and Weber, Ch. (1999). The asymptotic theory of permutation statistics. Math. Methods Statist. 8 220–250. Johann Pfanzagl—On the occasion of his 70th birthday.
  • Ugarte, M. D., Adin, A. and Goicoa, T. (2017b). One-dimensional, two-dimensional, and three dimensional B-splines to specify space-time interactions in Bayesian disease mapping: Model fitting and model identifiability. Spat. Stat. 22 451–468.
  • Wood, S. N., Scheipl, F. and Faraway, J. J. (2013). Straightforward intermediate rank tensor product smoothing in mixed models. Stat. Comput. 23 341–360.
  • Zeileis, A. and Hornik, K. (2007). Generalized $M$-fluctuation tests for parameter instability. Stat. Neerl. 61 488–508.
  • Zeileis, A., Hothorn, T. and Hornik, K. (2008). Model-based recursive partitioning. J. Comput. Graph. Statist. 17 492–514.

Supplemental materials

  • Supplement A: Different response distributions. To assess the goodness of fit of the Gaussian distribution, left-censored at zero, this supplement employs the same evaluations as in the main manuscript but based on two other distributional assumptions: A logistic distribution, left-censored at zero, is employed to potentially better capture heavy tails—and a two-part hurdle model combining a binary model for zero vs. positive precipitation and a Gaussian model, truncated at zero, for the positive precipitation observations.
  • Supplement B: Stationwise evaluation. To show that Axams is a fairly typical station and similar insights can be obtained for other stations as well, this supplement presents the same analysis as in Section 3.3 of the main manuscript for 14 further meteorological stations.