Short-Range Forecast Impact from Assimilation of GPS-IPW Observations into the Rapid Update Cycle
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Résumé
Abstract Integrated precipitable water (IPW) estimates derived from time delays in the arrival of global positioning system (GPS) satellite signals are a relatively recent, high-frequency source of atmospheric moisture information available for real-time data assimilation. Different experimental versions of the Rapid Update Cycle (RUC) have assimilated these observations to assess GPS-IPW impact on moisture forecasts. In these tests, GPS-IPW data have proven to be a useful real-time source of moisture information, leading to more accurate short-range moisture forecasts when added to other observations. A multiyear experiment with parallel (one with GPS-IPW processed 24 h after the fact, one without) 3-h cycles using the original 60-km RUC was run from 1999 to 2004 with verification of each cycle against rawinsonde observations. This experiment showed a steady increase in the positive impact in short-range relative humidity (RH) forecasts due to the GPS-IPW data as the number of observing sites increased from 18 to almost 300 (as of 2004) across the United States and Canada. Positive impact from GPS-IPW on 850–700-hPa RH forecasts was also evident in 6- and 12-h forecasts. The impact of GPS-IPW data was also examined on forecasts from the more recent 20-km RUC, including a 1-h assimilation cycle and improved assimilation and physical parameterizations, now using real-time GPS-IPW retrievals available 30 min after valid time. In a 3-month comparison during the March–May 2004 period, 20-km RUC cycles with and without assimilation of GPS-IPW were compared with IPW for 3-, 6-, 9-, and 12-h forecasts. Using this measure, assimilation of GPS-IPW data led to the strongest improvements in the 3- and 6-h forecasts and smaller but still evident improvements in 9- and 12-h forecasts. In a severe convective weather case, inclusion of GPS-IPW data improved forecasts of convective available potential energy, an important predictor of severe storm potential, and relative humidity. Positive impact from GPS-IPW assimilation was found to vary over season, geographical location, and time of day, apparently related to variations in vertical mixing. For example, GPS-IPW has a stronger effect on improving RH forecasts at 850 hPa at nighttime (than daytime) and in cooler seasons (than warmer seasons) when surface moisture observations are less representative of conditions aloft. As a result of these studies, assimilation of GPS-IPW was added to the operational RUC run at NOAA/NCEP in June 2005 and to the operational North American Mesoscale model (also at NCEP) in June 2006 to improve their accuracy for short-range moisture forecasts.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle