Concert en Ut majeur pour trois clavecins avec deux violons, viola, et basse.

Facsimile of manuscript.

Concerto à cembalo concertato 1mo. cembalo concertato 2o. 2. violini viola e basso /

Laid in are: photocopy (negative) of complete manuscript, and 20th cent. manuscript copy of violin, viola and basso parts (in the hand of J. A. Stellfeld?)

Concert (en Ut mineur) pour deux clavecins avec deux violons, viola, et basse.

Facsimile of manuscript.

Konzert, C moll, für Violine und Oboe oder für zwei Violinen mit Klavierbegleitung /

Publisher's no.: Edition Peters no. 3722.

Concerto a due violini concertini, 2 violini ripieni, viola, violone e organo, op. 8 /

Publisher's no.: Augener's edition 7417.

Three concerti for two pianos: Concerto in C minor; Concerto in C major; Concerto in C minor(revised)

Mode of access: Internet.

Russian suite for string orchestra with violin obligato ... Op. 81.

Mode of access: Internet.

Konzert, A moll, Klavier, konzertante Violine, Flöte und Streichorchester /

Reduction for 2 pianos.

Concert en ré majeur pour le clavecin avec accompagnement de deux violons, viola et basse.

Mode of access: Internet.

Clavier concerti. [music] /

[v.1.] Nr. 1, D moll. Nr. 2, E dur. Nr. 3, D dur. Nr. 4, A dur -- [v.2.] Nr. 5, F moll. Nr. 6, F dur. Nr. 7, G moll.

Triple concerto in A minor ; Old version of the first five clavier concerti. [music] /

Mode of access: Internet.

Concert en fa mineur pour le clavecin avec accompagnement de deux violons, viola et basse

Mode of access: Internet.

Musikalische Werke.

"Thematisches Verzeichniss der Flötensonaten": v. 1, p. xix-xxii.

[Suite for String Orchestra]

Soitinnus: jousiorkesteri.

A method for merging flow-dependent forecast error statistics from an ensemble with static statistics for use in high resolution variational data assimilation

The background error covariance matrix, B, is often used in variational data assimilation for numerical weather prediction as a static and hence poor approximation to the fully dynamic forecast error covariance matrix, Pf. In this paper the concept of an Ensemble Reduced Rank Kalman Filter (EnRRKF) is outlined. In the EnRRKF the forecast error statistics in a subspace defined by an ensemble of states forecast by the dynamic model are found. These statistics are merged in a formal way with the static statistics, which apply in the remainder of the space. The combined statistics may then be used in a variational data assimilation setting. It is hoped that the nonlinear error growth of small-scale weather systems will be accurately captured by the EnRRKF, to produce accurate analyses and ultimately improved forecasts of extreme events.