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Based on these sources of information, the user has to decide which time-series of monthly gravity fields to use. For most users, the peculiarities of the different processing approaches of the individual GRACE ACs remain unclear. Therefore, there is urgent need for a unification of gravity models like it is done for the products of other space geodetic techniques by, e.g., the International GNSS Service [IGS, Dow et al. ( 2009)], the International Laser Ranging Service [ILRS, Pearlman et al. ( 2002)], the International VLBI Service [IVS, Nothnagel et al. ( 2017)] or the International DORIS Service [IDS, Tavernier et al. ( 2005)].
In tough economic times, companies need new ways to innovate and drive revenues. By combining a product with a service, or vice versa, firms can improve their bottom and top lines. However, if a hybrid offering is to be successful, managers need to understand which of four potential combinations is most appropriate.For hydrological applications, monthly means of atmosphere [GAA, Flechtner and Dobslaw ( 2013)], ocean [GAB, Flechtner and Dobslaw ( 2013)] and the global isostatic adjustment (GIA) model LM17.3 Footnote 5 are subtracted. For oceanographic applications, monthly means of the atmosphere, the terrestrial water storage modeled by the WaterGAP Global Hydrological Model [WGHM; Döll et al. ( 2003)] and GIA (evaluated at the epochs of the monthly gravity fields) are subtracted. Ideally, the inverse of the normal matrix is the full error variance–covariance matrix of model parameters. In reality, ACs do not guarantee a proper scaling of normal matrices. Therefore, we estimate the weight factors to be used in combining the individual NEQs. We propose an alternative weighting scheme which is based on the noise levels of the individual solutions (Sect. 5). Relative weights are derived by VCE on the solution level (Jean et al. 2018) and then consequently applied to the NEQs. The main difference between the classical VCE on the NEQ level and the alternative VCE on the solution level is that in the latter case, while correlations between the unknown parameters are lost, the error assessment does not depend on the different error modeling and absorption strategies of the individual ACs, but on the differences to a (weighted) mean of the individual solutions. While the promise of combined offerings is great, it’s easy to get them wrong. The problem is that too many companies, expecting to catch the brass ring, don’t think through exactly how to structure, market, and sell their combined offerings. Over the past three years, the authors have analyzed more than 100 winning hybrid offerings from a variety of B2B and B2C companies. Their research shows that most companies stumble in at least one of four ways: failure to differentiate, failure to scale, failure to assess markets and prices appropriately, and failure to invest in the brand. Based on installing a new A-rated condensing boiler in England, Scotland and Wales with a programmer, room thermostat and thermostatic radiator controls (TRVs) in a gas heated detached home from an older G-rated boiler with a programmer and room thermostat. Figures are based on fuel prices as of October2023. Savings will vary depending on the size and thermal performance of your home. Source: Energy Saving Trust.
The standard approach is to expand the gravity field in spherical harmonics and provide the weight coefficients of this expansion (L2-products) that may be transformed to global grids (L3-products) for easier use. Depending on the field of application, the grids are complemented by monthly mean values of the short-term atmosphere and ocean mass variations [so-called GAX-products, Flechtner and Dobslaw ( 2013)] to restore the nontidal signal content. Moreover, the L3-products are usually pre-filtered to reduce noise.Four rules help managers discover which hybrid combination is most likely to be successful for their firm. The most obvious technique to estimate optimal weights is VCE. Unfortunately, a direct application of this technique, as it is presented in Appendix A.1, leads to suboptimal results, because the inversion of the NEQs provided by the ACs does not yield realistic covariance matrices of errors in model parameters. We therefore compute and apply additional empirical factors, which so to say equalize the available normal matrices. Those factors are estimated such that all the individual solutions contribute equally to the combined solution, independently of their quality. (This procedure is described in Sect. 5.2.)
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