GPS coordinate estimates by a priori tropospheric delays from NWP using ultra-rapid orbits

High accuracy GPS positioning estimates using scientific GPS software through three different processing strategies were compared. The two Italian baselines in a time period of 5 months during 2004 made a calculus data set. For high accuracy GPS differential positioning the use of global tropospheric delay models can be replaced by the implementation of other techniques. The GPS coordinate can be repeated when the tropospheric delay is calculated in Near-Real Time (NRT) from a Numerical Weather Prediction (NWP) model. For the NRT approach IGS ultra-rapid orbits instead of precise orbits were used. Concerning coordinate repeatability, the NWP-based strategy with tropospheric error adjustment appeared more accurate (at the submillimetric level) than a standard GPS strategy. Furthermore, several hundreds km long baselines demonstrated the standard deviation at the level of millimeters (from 4.2 to 7.6 mm). Practically, the NWP-based strategy offers the advantage of tropospheric delay estimations closer to realistic meteorological values. The application of a more accurate meteorology leads to satisfactory coordinate estimations, and vice versa well-defined GPS estimations of coordinates may serve as the additional meteorological parameters source

GPS Zenith Total Delays and precipitable water in comparison with special meteorological observations in Verona (Italy) during MAP-SOP

Continuous meteorological examination of the Pre-Alpine zones in Northern Italy (Po Valley) is important for determination of atmospheric water cycles connected kith floods and rainfalls. During a special meteorological observing period (MAP-SOP). radiosounding and other measurements were made in the site of Verona (Italy), This paper deals with Zenith Total Delay (ZTD) and Precipitable Water (PW) comparisons obtained by GPS, radiosounding and other meteorological measurements. PW and ZTD from ground-based GPS data in comparison with classical techniques (e.g.. WVR, radiosounding,) from recent literature present an accurate tool for use in meteorology applications (e.g., assimilation in Numerical Weather Prediction (NWP) models oil short-range precipitation forecasts). Comparison of such ZTD for MAP-SOP showed a standard deviation of 16.1 mm and PW comparison showed a standard deviation of 2.7 mm, confirming the accuracy of GPS measurements for meteorology applications. In addition, PW data and its time variation are also matched with time series of meteorological situations. Those results indicate that changes in PW values could be connected to changes in air masses, i.e. to passages of both cold and warm fronts. There is also a correlation between precipitation. forthcoming increase and the following decrease of PW. A good agreement between oscillation of PW and precipitation and strong cyclonic activities is found

Detection of meteorological inconsistencies by GPS

GPS observations, distances from satellites to receivers and meteorological conditions in neutral atmosphere are known to obey a constraint, which provides a residual or in other words a quality index. A method is discussed which provides a residual epoch by epoch in near real time. In general, distribution of residuals during several consecutive epochs belonging to the same satellites, allows estimates of a mean and a standard deviation of mean. Under normal meteorological conditions distribution of residuals appears to be consistent with zero mean as expected. However, consecutive residuals sometimes appear to have a mean different from zero by more than three standard deviations of mean. Such signifi cant consecutive epochs provide a warning of existing inconsistencies among GPS observations, distances from satellites to receivers as obtained by orbital information, meteorological conditions above receivers (as obtained by ground measurements or by extrapolation of meteorological analysis). A procedure has been set up which warns about these inconsistencies in near real time

Practice patterns and 90-day treatment-related morbidity in early-stage cervical cancer

To evaluate the impact of the Laparoscopic Approach to Cervical Cancer (LACC) Trial on patterns of care and surgery-related morbidity in early-stage cervical cancer

Urn model for daily hydric precipitation

The aim of this paper is to present a statistical model which could exploit further dynamical information, capable to give a better forecast of daily hydric precipitation. The two variables, amount of water (q) and number of dry days which precede the wet day (d), appeared reasonably independent. The statistical distribution of q is well fitted by a Gamma distribution. Each day is considered wet or dry according to the result of a drawing from an urn. The composition of the urn changes day to day as in the contagious models

Soluzioni particolari per fluidi anisotropi in verticale.

Viene introdotto nelle equazioni della idrodinamica un tensore degli sforzi modificato avente una traccia uguale a -3p. Trascurando la forza di Coriolis sono state ottenute alcune soluzioni particolari aventi un andamento esponenziale in dipendenza della quota. E’ ipotizzabile utilizzare tali soluzioni particolari nella descrizione dello strato superficiale dell’atmosfera

Statistical model for daily precipitation

Daily records of precipitation measured in Modena (Po Plain in Italy) since March 1830, were analyzed in the framework of a stochastic bivariate model. The following stochastic variables were chosen: q, equivalent height of water precipitated during the wet day; d, number of dry days which precede the wet day. The two variables appear reasonably independent so that, in a wet day, the overall probability that the next wet day is labelled by q and d is given by the product of two independent probabilities. The probability that the next wet day occurs after d+1 days follows a Polya distribution. This distribution has two parameters which show a seasonal dependence. The probability distribution of the equivalent height of precipitated water, q follows a gamma distribution. This distribution has also two parameters which show a seasonal dependence

Statistical forecasting of daily precipitations

From the time series of daily precipitation observed in Modena (Italy) since 1830, a model for the daily statistical forecasting has been built. The main tool of the model is an urn which contains balls labelled by «wet» and «dry». The daily extraction from the urn determines whether, on that day, there will be a precipitation,i.e. if that day will be «wet» or «dry». If the day is dry, the content of the urn is changed by adding other balls some labelled by dry and some other labelled by wet. The numbers of added balls depend on the day of the year (seasonal dependence). If the day is wet, the urn is reset to an initial condition, which depends on the day of the year (seasonal dependence). Moreover, if the day was wet, the likely quantity of precipitation is deduced from a gamma distribution with parameters which are seasonally dependent. All the seasonally dependent parameters in the statistical processes previously discussed can be expressed by Fourier expansions, having one year as fundamental period. The observed distributions are adequately fitted by expansions which do not exceed the second harmonic. Although the model has been tuned on the observations in Modena, it can presumably be extended to the entire region having same climate,i.e. the Po Plain. È presentato un modello per la previsione statistica giornaliera di precipitazione, messo a punto mediante la serie temporale delle osservazioni giornaliere a Modena (Italia) che inizia dal 1830. Si tratta di un modello ad «urna» la quale contiene palline etichettate «pioggia» e «secco». L'estrazione giornaliera dall'urna determina se in quel giorno ci sarà precipitazione, ossia se il giorno è di pioggia» o «secco». Se il giorno è secco, il contenuto dell'urna è variato con l'aggiunta di altre palline etichettate da «secco» e «pioggia». Il numero di palline aggiunte dipende dal giorno dell'anno (dipendenza stagionale). Se il giorno è piovoso, il contenuto dell'urna è riportato ad una condizione iniziale, che dipende dal giorno dell'anno (dipendenza stagionale). Inoltre, nel caso di giorno piovoso, la quantità di pioggia è dedotta da una distribuzione gamma con parametri dipendenti dal tempo. Tutti i parametri con dipendenza stagionale del precedente processo statistico sono stati espressi mediante uno sviluppo in serie di Fourier, avente un periodo fondamentale di un anno. Le distribuzioni osservate sono adeguatamente rappresentate da sviluppi in serie che non superano la seconda armonica. Questo metodo di previsione statistica giornaliera può essere facilmente combinato con metodi di previsione giornaliera dinamica, poiché le previsioni (sia statistica che dinamica) sono effettuate ogni giorno. Sebbene il modello sia stato messo a punto mediante le osservazioni di precipitazione a Modena, presumibilmente esso può essere esteso all'intera regione climaticamente omogenea, ossia la Pianura Padana. Из временной последовательности суточных выпадений осадков, зарегистрированных в Модене (Италия) с 1830 г., построена модель для суточного статистического прогнозирования. Основной инструмент модели представляет урна, которая содержит шары с метками «мокрый» и «сухой». Суточное извлечение из урны определяет прогнозирование на этот день, т.е. будет ли этот день «мокрый» или «сухой». Если день «сухой», то содержание урны изменяется посредством добавления шаров, часть из которых помечена «сухими» и другая часть помечена «мокрыми». Число добавленных шаров зависит от дня в году (сезонная зависимость). Если день «мокрый», то урна возвращается в начальное состояние, которое зависит от дня в году (сезонная зависимость). Кроме того, если день «мокрый», то вероятность прогнозирования выводится из гамма-распределения с параметрами, которые зависят от времени года. Все зависящие от сезона параметры в статистических процессах, которые ранее обсуждались, могут быть выражены с помощью Фурье-разложений, которые имеют основной период, равный одному году. Наблюденные распределения адекватно описываются с помошью разложеннй, которые не превышают вторых гармоник

Particular exact solutions for a small area

Particular exact solutions of the hydrodynamic equations for a small sea are presented. They have been obtained under the following assumptions: sea on Earth's tangent plane; constant density; viscous-like stress tensor made up by an isotropic part and two other parts only horizontally isotropic; traceless and dissipative viscous-like stress tensor. In the search of particular exact solutions a supplementary hypothesis has been assumed: the advective part results from the gradient of suitable potentials (kinetic energy and rotational potential). Since the exact solution is considered as an initial solution in a perturbation approximation, the small Coriolis force has been neglected. For a small rectangular sea as the Adriatic sea, the solutions satisfy the conditions on the fixed boundaries (bottom and coastlines of the sea) with a very good approximation. The conditions at the free surface will be also discussed

Struttura statistica delle precipitazioni

Sono stati analizzati dati di precipitazioni idriche, registrati giornalmente dall'Osservatorio Geofisico fin dal 1830. Una serie temporale così lunga permette la messa a punto di un modello statistico in grado di sintetizzare l'andamento dei dati mediante un'espressione di probabilità comprendente pochi parametri. In prima approssimazione la serie temporale dei dati è stata considerata ciclostazionaria. Inoltre è stato scelto di caratterizzare ogni giorno con precipitazione misurabile mediante tre variabili: la quantità di acqua precipitata, q, il numero di giorni secchi che hanno preceduto il giorno con pioggia, d,il numero d'ordine del giorno considerato entro ogni anno. Il modello ipotizzato è bivariato in q e d. La distribuzione di probabilità della variabile q segue una distribuzione gamma, mentre quella della variabile d approssima una distribuzione di Polya. La rappresentazione della probabilità tramite una distribuzione di Polya non è unica. Si è trovato che tale probabilità può essere espressa da un modello ad urna la cui composizione cambia ogni giorno