Thomas Nipen

• Doctor of Philosophy
• Researcher at Norwegian Meteorological Institute

A data-driven model (DDM) suitable for regional weather forecasting applications is presented. The model extends the Artificial Intelligence Forecasting System by introducing a stretched-grid architecture that dedicates higher resolution over a regional area of interest and maintains a lower resolution elsewhere on the globe. The model is based on...

During the last 2 years, tremendous progress has been made in global data-driven weather models trained on numerical weather prediction (NWP) reanalysis data. The most recent models trained on the ERA5 reanalysis at 0.25° resolution demonstrate forecast quality on par with ECMWF's high-resolution model with respect to a wide selection of verificati...

MET Nordic is a dataset of near-surface variables for Scandinavia, Finland, and the Baltic countries at 1 km resolution produced by the Norwegian Meteorological Institute (MET Norway). The dataset goes back to 2012 and it is updated in real time every hour. The MET Nordic dataset consists of post-processed products that (a) describe the current and...

During the last two years, tremendous progress in global data-driven weather models trained on numerical weather prediction (NWP) re-analysis data has been made. The most recent models trained on the ERA5 at 0.25{\deg} resolution demonstrate forecast quality on par with ECMWF's high-resolution model with respect to a wide selection of verification...

Verif is an open-source tool for verifying weather predictions against a ground truth. It is suitable for a range of applications and designed for iterative product development involving fine-tuning of algorithms, comparing methods, and addressing scientific issues with the product. The tool generates verification plots based on user-supplied input...

We present a comparison between Netatmo hourly precipitation amounts and observations of the same quantity from weather stations managed by national meteorological services, the latter used as reference values. The empirical distributions of the crowdsourced observations in the surroundings of reference stations are used to assess accuracy and prec...

Crowdsourced meteorological observations are becoming more prevalent and in some countries their spatial resolution already far exceeds that of traditional networks. However, due to the larger uncertainty associated with these observations, quality control (QC) is an essential step. Spatial QC methods are especially well-suited for such dense netwo...

Hourly precipitation over a region is often simultaneously simulated by numerical models and observed by multiple data sources. An accurate precipitation representation based on all available information is a valuable result for numerous applications and a critical aspect of climate monitoring. The inverse problem theory offers an ideal framework f...

Sample sizes of observed climate extremes are typically too small to reliably constrain return period estimates when there is non-stationary behaviour. To increase the historical record 100-fold, we apply the UNprecedented Simulated Extreme ENsemble (UNSEEN) approach, by pooling ensemble members and lead times from the ECMWF seasonal prediction sys...

In science, poor quality input data will invariably lead to faulty conclusions, as in the spirit of the saying “garbage in, garbage out”. Atmospheric sciences make no exception and correct data is crucial to obtain a useful representation of the real world in meteorological, climatological and hydrological applications. Titan is a computer program...

Hourly precipitation over a region is often simultaneously simulated by numerical models and observed by multiple data sources. An accurate precipitation representation based on all available information is a valuable result for numerous applications and a critical aspect of climate. Inverse problem theory offers an ideal framework for the combinat...

Sample sizes of observed climate extremes are typically too small to reliably constrain non-stationary behaviour. To facilitate detection of non-stationarities in 100-year precipitation values over a short period of 35 years (1981-2015), we apply the UNprecedented Simulated Extreme ENsemble (UNSEEN) approach, by pooling ensemble members and lead ti...

Please find the presentation on the EGU website, by clicking on the DOI. Take away points: 1. Using hindcasts, we can generate 100 'alternate realities' of the last 35 years 2. We can do this because the forecasts are independent and stable over lead times 3. Our results highlight the strength of UNSEEN-trends in quantifying and explaining rare...

Accurate hourly two‐metre temperature gridded fields available in near real‐time are valuable products for numerous applications, such as civil protection and energy production planning. An analysis ensemble of temperature is obtained from the combination of a numerical weather prediction ensemble (background) and in situ observations. At the core...

Citizen weather stations are rapidly increasing in prevalence and are becoming an emerging source of weather information. These low-cost consume-rgrade devices provide observations in real time and form parts of dense networks that capture high-resolution meteorological information. Despite these benefits, their adoption into operational weather pr...

A climatology of extreme cold season precipitation events in Norway from 1979 to 2014 is presented, based on the 99th percentile of the 24-h accumulated precipitation. Three regions, termed north, west, and south are identified, each exhibiting a unique seasonal distribution. There is a proclivity for events to occur during the positive phase of th...

A climatology of extreme cold season precipitation events in Norway from 1979-2014 is presented, based on the 99th percentile of the 24-hour accumulated precipitation. Three regions, termed North, West and South are identified , each exhibiting a unique seasonal distribution. There is a proclivity for events to occur during the positive phase of th...

The WMO WWRP project FROST-2014 (FROST - Forecast and Research in the Olympic Sochi Testbed) was targeted at the advancement and demonstration of state-of-the art nowcasting and short-range forecasting systems for winter conditions in mountainous terrain. The project field campaign was held during the 2014 XXII Olympic and XI Paralympic Winter Game...

This work evaluates the use of a WRF ensemble for short-term, probabilistic, hub-height wind speed forecasts in complex terrain. Testing for probabilistic-forecast improvements is conducted by increasing the number of planetary boundary layer schemes used in the ensemble. Additionally, several prescribed uncertainty models used to derive forecast p...

Three ensemble prediction systems (EPSs) with different grid spacings are compared and evaluated with respect to their ability to predict wintertime weather in complex terrain. The experiment period was two-and-a-half winter months in 2014, coinciding with the Forecast and Research in the Olympic Sochi Testbed (FROST) project, which took place duri...

An accurate representation of the precipitation field available on a hourly basis at high spatial resolution is of paramount importance for climatological, meteorological and hydrological applications. On the Norwegian mainland, most applications require a reliable representation of the atmospheric dynamics down to the lower bound of the Meso-scale...

An analysis of the impacts of assimilating the Tropospheric Airborne Meteorological Data Report (TAMDAR) data with the Weather Research and Forecasting- (WRF-) real-time four-dimensional data assimilation (RTFDDA) and forecasting system over the Contiguous US (CONUS) is presented. The impacts of the horizontal resolution increase from 12 km to 4 km...

Recently, two analog-based postprocessing methods were demonstrated to reduce the systematic and random errors from Weather Research and Forecasting (WRF) Model predictions of 10-m wind speed over the central United States. To test robustness and generality, and to gain a deeper understanding of postprocessing forecasts with analogs, this paper exp...

This paper describes a probabilistic reservoir inflow forecasting system that explicitly attempts to sample from major sources of uncertainty in the modelling chain. Uncertainty in hydrologic forecasts arises due to errors in the hydrologic models themselves, their parameterizations, and in the initial and boundary conditions (e.g., meteorological...

Two new postprocessing methods are proposed to reduce numerical weather prediction's systematic and random errors. The first method consists of running a postprocessing algorithm inspired by the Kalman filter (KF) through an ordered set of analog forecasts rather than a sequence of forecasts in time (ANKF). The analog of a forecast for a given loca...

ABSTRACTA post-processing method for calibrating probabilistic forecasts of continuous weather variables is presented. The method takes an existing probability distribution and adjusts it such that it becomes calibrated in the long run. The original probability distributions can be ones such as are generated from a numerical weather prediction (NWP...

A post-processing method for calibrating probabilistic forecasts of continuous weather variables is presented. The method takes an existing probability distribution and adjusts it such that it becomes calibrated in the long run. The original probability distributions can be ones such as are generated from a numerical weather prediction (NWP) ensemb...

A statistical postprocessing method for improving probabilistic forecasts of continuous weather variables, given recent observations, is presented. The method updates an existing probabilistic forecast by incorporating observations reported in the intermediary time since model initialization. As such, this method provides updated short-range probab...

The Kalman filter (KF) is a recursive algorithm to estimate a signal from noisy measurements. In this study it is tested in predictor mode, to postprocess ozone forecasts to remove systematic errors. The recent past forecasts and observations are used by the KF to estimate the future bias. This bias correction is calculated separately for, and appl...