Scale-Dependence of the Predictability of Precipitation from Continental Radar Images. Part I: Description of the Methodology

By Urs Germann, I. Zawadzki. 2002

421 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atmospheric Science

1. Elizabeth E. Ebert
2. Barbara Casati
3. Juanzhen Sun
4. I. Zawadzki
5. B. Brown
6. Neill Bowler
7. C. Pierce
8. Q. Xiao

In Global and Planetary Change

1. Barbara Casati
2. Elizabeth E. Ebert
3. Juanzhen Sun
4. I. Zawadzki
5. Neill Bowler
6. C. Pierce
7. B. Brown
8. Q. Xiao

Citations in the corpus

• Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting (2015, methodology): “Specifically, some computer vision techniques, especially optical flow based methods, have proven useful for making accurate extrapolation of radar maps [10, 6, 20].”
• Multiple-Radar Data Assimilation and Short-Range Quantitative Precipitation Forecasting of a Squall Line Observed during IHOP_2002 (2007, methodology): “The echo patterns and Doppler velocity wind syntheses have been compared with numerical simulations in order to verify whether the simulations generated realistic results in a few studies (Germann and Zawadski 2002; Sun 2005).”
• McGill algorithm for precipitation nowcasting by lagrangian extrapolation (MAPLE) applied to the South Korean radar network. Part I: Sensitivity studies of the Variational Echo Tracking (VET) technique (2009, methodology): “A basic description of MAPLE is provided by Germann and Zawadzki (2002), the extension to probabilistic forecasts has then been formulated by Germann and Zawadzki (2004), the implementation of a filtered forecast that removes the perishable scales has been carried out by Seed (2003) and by Turner…”
• Forecast verification: current status and future directions (2008, background): “Germann and Zawadzki (2002, 2004), and Turner et al. (2004) use a similar error-decomposition approach for the MAPLE nowcasting system and study the scale-dependence of precipitation predictability by using wavelets.”
• STEPS: A probabilistic precipitation forecasting scheme which merges an extrapolation nowcast with downscaled NWP (2006, background): “Deterministic nowcasting models have been shown to exhibit useful predictive skill at lead times of many hours (Germann and Zawadzki 2002).”
• Spatial–temporal mesoscale modeling of rainfall intensity using gage and radar data (2008, background): “The reflectivity–rainfall conversion function is modeled in the natural logarithm scale, because the error component is thought to increase with the precipitation values, and in the original scale too much weight is given to high rainfall rates [Lee and Zawadzki (2005), Germann and Zawadzki (2002)].”